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import time
import cv2
import numpy as np
import torch
import torch.cuda.amp as amp
import torch.distributed as dist
import torch.nn.functional as F
import wandb
from loguru import logger
from tqdm import tqdm
import matplotlib.pyplot as plt
from utils.dataset import tokenize
from utils.misc import (AverageMeter, ProgressMeter, concat_all_gather, trainMetricGPU, get_seg_image)
from utils.grasp_eval import (ssg_post_processing, detect_grasps, calculate_iou, calculate_max_iou, calculate_jacquard_index, visualization)
def train(train_loader, model, optimizer, scheduler, scaler, epoch, args):
batch_time = AverageMeter('Batch', ':2.2f')
data_time = AverageMeter('Data', ':2.2f')
lr = AverageMeter('Lr', ':1.6f')
cls_loss_meter = AverageMeter('Loss_cls', ':2.4f')
box_loss_meter = AverageMeter('Loss_box', ':2.4f')
ins_loss_meter = AverageMeter('Loss_ins', ':2.4f')
sem_loss_meter = AverageMeter('Loss_sem', ':2.4f')
qua_loss_metter = AverageMeter('Loss_qua', ':2.4f')
sin_loss_metter = AverageMeter('Loss_sin', ':2.4f')
cos_loss_metter = AverageMeter('Loss_cos', ':2.4f')
wid_loss_metter = AverageMeter('Loss_wid', ':2.4f')
iou_meter = AverageMeter('IoU', ':2.2f')
pr_meter = AverageMeter('Prec@50', ':2.2f')
progress = ProgressMeter(
len(train_loader),
[
batch_time, data_time, lr,
cls_loss_meter, box_loss_meter, ins_loss_meter, sem_loss_meter,
qua_loss_metter, sin_loss_metter, cos_loss_metter, wid_loss_metter,
iou_meter, pr_meter
],
prefix="Training: Epoch=[{}/{}] ".format(epoch, args.epochs))
model.train()
time.sleep(2)
end = time.time()
for i, data_dict in enumerate(train_loader):
data_dict["rgb"] = data_dict["rgb"].cuda(non_blocking=True)
data_dict["depth"] = data_dict["depth"].cuda(non_blocking=True)
data_dict["labels"] = [x.cuda(non_blocking=True) for x in data_dict["labels"]]
data_dict["bboxes"] = [x.cuda(non_blocking=True) for x in data_dict["bboxes"]]
data_dict["sem_mask"] = [x.cuda(non_blocking=True) for x in data_dict["sem_mask"]]
data_dict["ins_masks"] = [x.cuda(non_blocking=True) for x in data_dict["ins_masks"]]
data_dict["grasp_masks"]["qua"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["qua"]]
data_dict["grasp_masks"]["sin"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["sin"]]
data_dict["grasp_masks"]["cos"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["cos"]]
data_dict["grasp_masks"]["wid"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["wid"]]
output_dict, loss_dict = model(data_dict)
loss = 0.0
for k, v in loss_dict.items():
loss += v
optimizer.zero_grad()
loss.backward()
if args.max_norm:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
optimizer.step()
iou, pr5 = trainMetricGPU(output_dict["inter_mask_p"], output_dict["inter_mask_gt"], 0.35, 0.5, sigmoid=False)
dist.all_reduce(loss.detach())
dist.all_reduce(iou)
dist.all_reduce(pr5)
loss = loss / dist.get_world_size()
iou = iou / dist.get_world_size()
pr5 = pr5 / dist.get_world_size()
cls_loss_meter.update(loss_dict["loss_cls"].item(), data_dict["rgb"].size(0))
box_loss_meter.update(loss_dict["loss_box"].item(), data_dict["rgb"].size(0))
ins_loss_meter.update(loss_dict["loss_ins"].item(), data_dict["rgb"].size(0))
sem_loss_meter.update(loss_dict["loss_sem"].item(), data_dict["rgb"].size(0))
qua_loss_metter.update(loss_dict["loss_qua"].item(), data_dict["rgb"].size(0))
sin_loss_metter.update(loss_dict["loss_sin"].item(), data_dict["rgb"].size(0))
cos_loss_metter.update(loss_dict["loss_cos"].item(), data_dict["rgb"].size(0))
wid_loss_metter.update(loss_dict["loss_wid"].item(), data_dict["rgb"].size(0))
iou_meter.update(iou, data_dict["rgb"].size(0)) #@TODO add iou & pr evaluation
pr_meter.update(pr5, data_dict["rgb"].size(0))
lr.update(scheduler.get_last_lr()[-1])
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
progress.display(i + 1)
if dist.get_rank() in [-1, 0]:
wandb.log(
{
"time/batch": batch_time.val,
"time/data": data_time.val,
"training/lr": lr.val,
"training/loss_cls": cls_loss_meter.val,
"training/loss_box": box_loss_meter.val,
"training/loss_ins": ins_loss_meter.val,
"training/loss_sem": sem_loss_meter.val,
"training/loss_qua": qua_loss_metter.val,
"training/loss_sin": sin_loss_metter.val,
"training/loss_cos": cos_loss_metter.val,
"training/loss_wid": wid_loss_metter.val,
"training/iou": iou_meter.val,
"training/prec@50": pr_meter.val,
},
step=epoch * len(train_loader) + (i + 1))
@torch.no_grad()
def validate(val_loader, model, epoch, args):
model.eval()
time.sleep(2)
num_grasps = [1,5]
J_index = [0, 0]
num_correct_grasps = [0, 0]
num_total_grasps = [0, 0]
for i, data_dict in enumerate(val_loader):
data_dict["rgb"] = data_dict["rgb"].cuda(non_blocking=True)
data_dict["depth"] = data_dict["depth"].cuda(non_blocking=True)
data_dict["labels"] = [x.cuda(non_blocking=True) for x in data_dict["labels"]]
data_dict["bboxes"] = [x.cuda(non_blocking=True) for x in data_dict["bboxes"]]
data_dict["sem_mask"] = [x.cuda(non_blocking=True) for x in data_dict["sem_mask"]]
data_dict["ins_masks"] = [x.cuda(non_blocking=True) for x in data_dict["ins_masks"]]
data_dict["grasp_masks"]["qua"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["qua"]]
data_dict["grasp_masks"]["sin"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["sin"]]
data_dict["grasp_masks"]["cos"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["cos"]]
data_dict["grasp_masks"]["wid"] = [x.cuda(non_blocking=True) for x in data_dict["grasp_masks"]["wid"]]
output_dict = model(data_dict)
post_dict = ssg_post_processing(args, output_dict, data_dict)
for idx, num_g in enumerate(num_grasps):
for obj_grasp_rects_gt in data_dict["grasp_rects"][0]:
flag_top1 = False
for obj_grasp_rects_p in post_dict["grasps_top1"]:
j_index_top1 = calculate_jacquard_index(obj_grasp_rects_p, obj_grasp_rects_gt)
if j_index_top1 > 0:
flag_top1 = True
break
if flag_top1:
num_correct_grasps[0] += 1
num_total_grasps[0] += 1
flag_top5 = False
for obj_grasp_rects_p in post_dict["grasps_top5"]:
j_index_top5 = calculate_jacquard_index(obj_grasp_rects_p, obj_grasp_rects_gt)
if j_index_top5 > 0:
flag_top5 = True
break
if flag_top5:
num_correct_grasps[1] += 1
num_total_grasps[1] += 1
# Use first 100 samples for validation to save time
if i > 100:
break
J_index = [0, 0]
for i in range(len(num_grasps)):
J_index[i] = num_correct_grasps[i]/num_total_grasps[i]
head = 'Evaluation: Epoch=[{}/{}] J_index@1: {:.2f} J_index@5: {:.2f}'.format(
epoch, args.epochs, 100. * J_index[0], 100. * J_index[1])
logger.info(head)
return J_index
@torch.no_grad()
def visualization(dataset, model, epoch, args):
model.eval()
time.sleep(2)
tgt_dir = os.path.join(args.output_folder, "vis", f"epoch-{epoch}")
os.makedirs(tgt_dir, exist_ok=True)
vis_idx = np.random.randint(len(dataset), size=1)[0]
data = dataset[vis_idx]
data_dict = dataset.collate_fn([data])
data_dict["rgb"] = data_dict["rgb"].cuda()
data_dict["depth"] = data_dict["depth"].cuda()
output_dict = model(data_dict)
post_dict = ssg_post_processing(args, output_dict, data_dict)
cls_pred = post_dict["cls"]
bboxes_pred = post_dict["bboxes"]
ins_masks_pred = post_dict["ins_masks"]
qua_masks_pred, ang_masks_pred, wid_masks_pred = post_dict["grasp_masks"]
grasps_top1 = post_dict["grasps_top1"]
grasps_top5 = post_dict["grasps_top5"]
img_path = os.path.join(dataset.root_dir, data_dict["scene_id"][0], "rgb", data_dict["img_f"][0])
img = cv2.imread(img_path, cv2.COLOR_BGR2RGB)
for i in range(bboxes_pred.shape[0]):
tmp = img.copy()
cls_p = cls_pred[i]
bbox = bboxes_pred[i]
ins_mask = ins_masks_pred[i].astype(int)
qua_mask_p = qua_masks_pred[i]
ang_mask_p = ang_masks_pred[i]
wid_mask_p = wid_masks_pred[i]
grasp_top5_p = grasps_top5[i]
fig = plt.figure(figsize=(20, 10))
ax = fig.add_subplot(1, 4, 1)
ax.imshow(tmp[:, :, ::-1])
ax.set_title('RGB')
ax.axis('off')
mask = np.asarray([0, 0, 255]).reshape(1, 1, 3).repeat(480, axis=0).repeat(640, axis=1)
ins_mask = np.expand_dims(ins_mask, axis=-1).repeat(3, axis=-1)
mask *= ins_mask
img_with_mask = (tmp * 0.4 + mask * 0.6).astype(int)
ax = fig.add_subplot(1, 4, 2)
ax.imshow(img_with_mask[:, :, ::-1])
ax.set_title('ins mask')
ax.axis('off')
# Put bounding box
tmp = cv2.rectangle(tmp, (int(bbox[0]),int(bbox[1])), (int(bbox[2]),int(bbox[3])), (0,255,0), 2)
tmp = cv2.putText(tmp, dataset.idx_to_class[cls_p], (int(bbox[0]),int(bbox[1])-20), cv2.FONT_HERSHEY_PLAIN, 1, (0,0,0), 2, cv2.LINE_AA)
ax = fig.add_subplot(1, 4, 3)
ax.imshow(tmp[:, :, ::-1])
ax.set_title('bounding box')
ax.axis('off')
# Put grasp rects
grasps = dataset.grasp_transforms.inverse(grasp_top5_p)
for entry in grasps:
ptA, ptB, ptC, ptD = [list(map(int, pt.tolist())) for pt in entry]
tmp = cv2.line(tmp, ptA, ptB, (0,0,0xff), 2)
tmp = cv2.line(tmp, ptD, ptC, (0,0,0xff), 2)
tmp = cv2.line(tmp, ptB, ptC, (0xff,0,0), 2)
tmp = cv2.line(tmp, ptA, ptD, (0xff,0,0), 2)
ax = fig.add_subplot(1, 4, 4)
ax.imshow(tmp[:, :, ::-1])
ax.set_title('grasps')
ax.axis('off')
plt.savefig(os.path.join(tgt_dir, f"sample_{i}.png"))
plt.cla()
plt.clf()
plt.close()
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