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train_planercnn_custom.py
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train_planercnn_custom.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license
(https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch
from torch import optim
from torch.utils.data import DataLoader
import os
from tqdm import tqdm
import numpy as np
import cv2
import sys
from models.model import *
from models.refinement_net import *
from models.modules import *
from datasets_custom.plane_stero_custom_dataset import *
from utils import *
from visualize_utils import *
from evaluate_utils import *
from options import parse_args
from config import PlaneConfig
def train(options):
if not os.path.exists(options.checkpoint_dir):
os.system("mkdir -p %s"%options.checkpoint_dir)
pass
if not os.path.exists(options.test_dir):
os.system("mkdir -p %s"%options.test_dir)
pass
# setting for training are made
config = PlaneConfig(options)
dataset = PlaneDataset(options, config, split='train', random=False)
dataset_test = PlaneDataset(options, config, split='test', random=False)
print('the number of images', len(dataset))
dataloader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=1)
model = MaskRCNN(config)
refine_model = RefineModel(options)
model.cuda()
model.train()
refine_model.cuda()
refine_model.train()
if options.restore == 1:
## Resume training
print('restore')
model.load_state_dict(torch.load(options.checkpoint_dir + '/checkpoint.pth'))
refine_model.load_state_dict(torch.load(options.checkpoint_dir + '/checkpoint_refine.pth'))
elif options.restore == 2:
## Train upon Mask R-CNN weights
model_path = options.MaskRCNNPath
print("Loading pretrained weights ", model_path)
model.load_weights(model_path)
pass
print("x94028920")
if options.trainingMode != '':
## Specify which layers to train, default is "all"
layer_regex = {
## all layers but the backbone
"heads": r"(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
## From a specific Resnet stage and up
"3+": r"(fpn.C3.*)|(fpn.C4.*)|(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
"4+": r"(fpn.C4.*)|(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
"5+": r"(fpn.C5.*)|(fpn.P5\_.*)|(fpn.P4\_.*)|(fpn.P3\_.*)|(fpn.P2\_.*)|(rpn.*)|(classifier.*)|(mask.*)",
## All layers
"all": ".*",
"classifier": "(classifier.*)|(mask.*)|(depth.*)",
}
assert(options.trainingMode in layer_regex.keys())
layers = layer_regex[options.trainingMode]
model.set_trainable(layers)
pass
trainables_wo_bn = [param for name, param in model.named_parameters() if param.requires_grad and not 'bn' in name]
trainables_only_bn = [param for name, param in model.named_parameters() if param.requires_grad and 'bn' in name]
model_names = [name for name, param in model.named_parameters()]
for name, param in refine_model.named_parameters():
assert(name not in model_names)
continue
optimizer = optim.SGD([
{'params': trainables_wo_bn, 'weight_decay': 0.0001},
{'params': trainables_only_bn},
{'params': refine_model.parameters()}
], lr=options.LR, momentum=0.9)
if 'refine_only' in options.suffix:
optimizer = optim.Adam(refine_model.parameters(), lr = options.LR)
pass
if options.restore == 1 and os.path.exists(options.checkpoint_dir + '/optim.pth'):
optimizer.load_state_dict(torch.load(options.checkpoint_dir + '/optim.pth'))
pass
print("104")
for epoch in range(options.numEpochs):
print("epcoh reached")
epoch_losses = []
data_iterator = tqdm(dataloader, total=len(dataset) + 1)
optimizer.zero_grad()
print("110")
for sampleIndex, sample in enumerate(data_iterator):
print(sample)
print("002")
losses = []
input_pair = []
detection_pair = []
dicts_pair = []
print("reached 2")
camera = sample[30][0].cuda()
for indexOffset in [0, 13]:
images, image_metas, rpn_match, rpn_bbox, gt_class_ids, gt_boxes, gt_masks, gt_parameters, gt_depth, extrinsics, gt_plane, gt_segmentation, plane_indices = sample[indexOffset + 0].cuda(), sample[indexOffset + 1].numpy(), sample[indexOffset + 2].cuda(), sample[indexOffset + 3].cuda(), sample[indexOffset + 4].cuda(), sample[indexOffset + 5].cuda(), sample[indexOffset + 6].cuda(), sample[indexOffset + 7].cuda(), sample[indexOffset + 8].cuda(), sample[indexOffset + 9].cuda(), sample[indexOffset + 10].cuda(), sample[indexOffset + 11].cuda(), sample[indexOffset + 12].cuda()
if indexOffset == 13:
print("continuiing")
input_pair.append({'image': images, 'depth': gt_depth, 'mask': gt_masks, 'bbox': gt_boxes, 'extrinsics': extrinsics, 'segmentation': gt_segmentation, 'plane': gt_plane, 'camera': camera})
continue
print("reached 1")
print("images shape",images.shape)
rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask, target_parameters, mrcnn_parameters, detections, detection_masks, detection_gt_parameters, detection_gt_masks, rpn_rois, roi_features, roi_indices, feature_map, depth_np_pred = model.predict([images, image_metas, gt_class_ids, gt_boxes, gt_masks, gt_parameters, camera], mode='training_detection', use_nms=2, use_refinement='refinement' in options.suffix, return_feature_map=True)
rpn_class_loss, rpn_bbox_loss, mrcnn_class_loss, mrcnn_bbox_loss, mrcnn_mask_loss, mrcnn_parameter_loss = compute_losses(config, rpn_match, rpn_bbox, rpn_class_logits, rpn_pred_bbox, target_class_ids, mrcnn_class_logits, target_deltas, mrcnn_bbox, target_mask, mrcnn_mask, target_parameters, mrcnn_parameters)
losses += [rpn_class_loss + rpn_bbox_loss + mrcnn_class_loss + mrcnn_bbox_loss + mrcnn_mask_loss + mrcnn_parameter_loss]
if config.PREDICT_NORMAL_NP:
normal_np_pred = depth_np_pred[0, 1:]
depth_np_pred = depth_np_pred[:, 0]
gt_normal = gt_depth[0, 1:]
gt_depth = gt_depth[:, 0]
depth_np_loss = l1LossMask(depth_np_pred[:, 80:560], gt_depth[:, 80:560], (gt_depth[:, 80:560] > 1e-4).float())
normal_np_loss = l2LossMask(normal_np_pred[:, 80:560], gt_normal[:, 80:560], (torch.norm(gt_normal[:, 80:560], dim=0) > 1e-4).float())
losses.append(depth_np_loss)
losses.append(normal_np_loss)
print("depth ckpt1")
else:
depth_np_loss = l1LossMask(depth_np_pred[:, 80:560], gt_depth[:, 80:560], (gt_depth[:, 80:560] > 1e-4).float())
losses.append(depth_np_loss)
normal_np_pred = None
print("depth ckpt2")
pass
if len(detections) > 0:
detections, detection_masks = unmoldDetections(config, camera, detections, detection_masks, depth_np_pred, normal_np_pred, debug=False)
if 'refine_only' in options.suffix:
detections, detection_masks = detections.detach(), detection_masks.detach()
pass
XYZ_pred, detection_mask, plane_XYZ = calcXYZModule(config, camera, detections, detection_masks, depth_np_pred, return_individual=True)
detection_mask = detection_mask.unsqueeze(0)
else:
XYZ_pred = torch.zeros((3, config.IMAGE_MAX_DIM, config.IMAGE_MAX_DIM)).cuda()
detection_mask = torch.zeros((1, config.IMAGE_MAX_DIM, config.IMAGE_MAX_DIM)).cuda()
plane_XYZ = torch.zeros((1, 3, config.IMAGE_MAX_DIM, config.IMAGE_MAX_DIM)).cuda()
pass
print("E45")
input_pair.append({'image': images, 'depth': gt_depth, 'mask': gt_masks, 'bbox': gt_boxes, 'extrinsics': extrinsics, 'segmentation': gt_segmentation, 'parameters': detection_gt_parameters, 'plane': gt_plane, 'camera': camera})
detection_pair.append({'XYZ': XYZ_pred, 'depth': XYZ_pred[1:2], 'mask': detection_mask, 'detection': detections, 'masks': detection_masks, 'feature_map': feature_map[0], 'plane_XYZ': plane_XYZ, 'depth_np': depth_np_pred})
if 'depth' in options.suffix:
## Apply supervision on reconstructed depthmap (not used currently)
if len(detections) > 0:
background_mask = torch.clamp(1 - detection_masks.sum(0, keepdim=True), min=0)
all_masks = torch.cat([background_mask, detection_masks], dim=0)
all_masks = all_masks / all_masks.sum(0, keepdim=True)
all_depths = torch.cat([depth_np_pred, plane_XYZ[:, 1]], dim=0)
depth_loss = l1LossMask(torch.sum(torch.abs(all_depths[:, 80:560] - gt_depth[:, 80:560]) * all_masks[:, 80:560], dim=0), torch.zeros(config.IMAGE_MIN_DIM, config.IMAGE_MAX_DIM).cuda(), (gt_depth[0, 80:560] > 1e-4).float())
else:
depth_loss = l1LossMask(depth_np_pred[:, 80:560], gt_depth[:, 80:560], (gt_depth[:, 80:560] > 1e-4).float())
pass
losses.append(depth_loss)
pass
continue
print("E456")
if (len(detection_pair[0]['detection']) > 0 and len(detection_pair[0]['detection']) < 30) and 'refine' in options.suffix:
## Use refinement network
pose = sample[26][0].cuda()
pose = torch.cat([pose[0:3], pose[3:6] * pose[6]], dim=0)
pose_gt = torch.cat([pose[0:1], -pose[2:3], pose[1:2], pose[3:4], -pose[5:6], pose[4:5]], dim=0).unsqueeze(0)
camera = camera.unsqueeze(0)
c = 0
detection_dict, input_dict = detection_pair[c], input_pair[c]
detections = detection_dict['detection']
detection_masks = detection_dict['masks']
image = (input_dict['image'] + config.MEAN_PIXEL_TENSOR.view((-1, 1, 1))) / 255.0 - 0.5
image_2 = (input_pair[1 - c]['image'] + config.MEAN_PIXEL_TENSOR.view((-1, 1, 1))) / 255.0 - 0.5
depth_gt = input_dict['depth'].unsqueeze(1)
masks_inp = torch.cat([detection_masks.unsqueeze(1), detection_dict['plane_XYZ']], dim=1)
segmentation = input_dict['segmentation']
plane_depth = detection_dict['depth']
depth_np = detection_dict['depth_np']
if 'large' not in options.suffix:
## Use 256x192 instead of 640x480
detection_masks = torch.nn.functional.interpolate(detection_masks[:, 80:560].unsqueeze(1), size=(192, 256), mode='nearest').squeeze(1)
image = torch.nn.functional.interpolate(image[:, :, 80:560], size=(192, 256), mode='bilinear')
image_2 = torch.nn.functional.interpolate(image_2[:, :, 80:560], size=(192, 256), mode='bilinear')
masks_inp = torch.nn.functional.interpolate(masks_inp[:, :, 80:560], size=(192, 256), mode='bilinear')
depth_gt = torch.nn.functional.interpolate(depth_gt[:, :, 80:560], size=(192, 256), mode='nearest')
segmentation = torch.nn.functional.interpolate(segmentation[:, 80:560].unsqueeze(1).float(), size=(192, 256), mode='nearest').squeeze().long()
plane_depth = torch.nn.functional.interpolate(plane_depth[:, 80:560].unsqueeze(1).float(), size=(192, 256), mode='bilinear').squeeze(1)
depth_np = torch.nn.functional.interpolate(depth_np[:, 80:560].unsqueeze(1), size=(192, 256), mode='bilinear').squeeze(1)
else:
detection_masks = detection_masks[:, 80:560]
image = image[:, :, 80:560]
image_2 = image_2[:, :, 80:560]
masks_inp = masks_inp[:, :, 80:560]
depth_gt = depth_gt[:, :, 80:560]
segmentation = segmentation[:, 80:560]
plane_depth = plane_depth[:, 80:560]
depth_np = depth_np[:, 80:560]
pass
depth_inv = invertDepth(depth_gt)
depth_inv_small = depth_inv[:, :, ::4, ::4].contiguous()
## Generate supervision target for the refinement network
segmentation_one_hot = (segmentation == torch.arange(segmentation.max() + 1).cuda().view((-1, 1, 1, 1))).long()
intersection = (torch.round(detection_masks).long() * segmentation_one_hot).sum(-1).sum(-1)
max_intersection, segments_gt = intersection.max(0)
mapping = intersection.max(1)[1]
detection_areas = detection_masks.sum(-1).sum(-1)
valid_mask = (mapping[segments_gt] == torch.arange(len(segments_gt)).cuda()).float()
masks_gt_large = (segmentation == segments_gt.view((-1, 1, 1))).float()
masks_gt_small = masks_gt_large[:, ::4, ::4]
planes_gt = input_dict['plane'][0][segments_gt]
print("E459")
## Run the refinement network
results = refine_model(image, image_2, camera, masks_inp, detection_dict['detection'][:, 6:9], plane_depth, depth_np)
plane_depth_loss = torch.zeros(1).cuda()
depth_loss = torch.zeros(1).cuda()
plane_loss = torch.zeros(1).cuda()
mask_loss = torch.zeros(1).cuda()
flow_loss = torch.zeros(1).cuda()
flow_confidence_loss = torch.zeros(1).cuda()
pose_loss = torch.zeros(1).cuda()
for resultIndex, result in enumerate(results[1:]):
if 'mask' in result:
masks_pred = result['mask'][:, 0]
if masks_pred.shape[-1] == masks_gt_large.shape[-1]:
masks_gt = masks_gt_large
else:
masks_gt = masks_gt_small
pass
all_masks_gt = torch.cat([1 - masks_gt.max(dim=0, keepdim=True)[0], masks_gt], dim=0)
segmentation = all_masks_gt.max(0)[1].view(-1)
masks_logits = masks_pred.squeeze(1).transpose(0, 1).transpose(1, 2).contiguous().view((segmentation.shape[0], -1))
detection_areas = all_masks_gt.sum(-1).sum(-1)
detection_weight = detection_areas / detection_areas.sum()
detection_weight = -torch.log(torch.clamp(detection_weight, min=1e-4, max=1 - 1e-4))
if 'weight' in options.suffix:
mask_loss += torch.nn.functional.cross_entropy(masks_logits, segmentation, weight=detection_weight)
else:
mask_loss += torch.nn.functional.cross_entropy(masks_logits, segmentation, weight=torch.cat([torch.ones(1).cuda(), valid_mask], dim=0))
pass
masks_pred = (masks_pred.max(0, keepdim=True)[1] == torch.arange(len(masks_pred)).cuda().long().view((-1, 1, 1))).float()[1:]
pass
continue
losses += [mask_loss + depth_loss + plane_depth_loss + plane_loss]
masks = results[-1]['mask'].squeeze(1)
all_masks = torch.softmax(masks, dim=0)
masks_small = all_masks[1:]
all_masks = torch.nn.functional.interpolate(all_masks.unsqueeze(1), size=(480, 640), mode='bilinear').squeeze(1)
all_masks = (all_masks.max(0, keepdim=True)[1] == torch.arange(len(all_masks)).cuda().long().view((-1, 1, 1))).float()
masks = all_masks[1:]
detection_masks = torch.zeros(detection_dict['masks'].shape).cuda()
detection_masks[:, 80:560] = masks
detection_dict['masks'] = detection_masks
results[-1]['mask'] = masks_small
camera = camera.squeeze(0)
if 'refine_after' in options.suffix:
## Build the warping loss upon refined results
XYZ_pred, detection_mask, plane_XYZ = calcXYZModule(config, camera, detections, detection_masks, detection_dict['depth_np'], return_individual=True)
detection_dict['XYZ'] = XYZ_pred
pass
else:
losses += [torch.zeros(1).cuda()]
pass
## The warping loss
for c in range(1, 2):
if 'warping' not in options.suffix:
break
detection_dict = detection_pair[1 - c]
neighbor_info = torch.cat([detection_dict['XYZ'], detection_dict['mask'], input_pair[1 - c]['image'][0]], dim=0).unsqueeze(0)
warped_info, valid_mask = warpModuleDepth(config, camera, input_pair[c]['depth'][0], neighbor_info, input_pair[c]['extrinsics'][0], input_pair[1 - c]['extrinsics'][0], width=config.IMAGE_MAX_DIM, height=config.IMAGE_MIN_DIM)
XYZ = warped_info[:3].view((3, -1))
XYZ = torch.cat([XYZ, torch.ones((1, int(XYZ.shape[1]))).cuda()], dim=0)
transformed_XYZ = torch.matmul(input_pair[c]['extrinsics'][0], torch.matmul(input_pair[1 - c]['extrinsics'][0].inverse(), XYZ))
transformed_XYZ = transformed_XYZ[:3].view(detection_dict['XYZ'].shape)
warped_depth = transformed_XYZ[1:2]
warped_images = warped_info[4:7].unsqueeze(0)
warped_mask = warped_info[3]
with torch.no_grad():
valid_mask = valid_mask * (input_pair[c]['depth'] > 1e-4).float()
pass
warped_depth_loss = l1LossMask(warped_depth, input_pair[c]['depth'], valid_mask)
losses += [warped_depth_loss]
if 'warping1' in options.suffix or 'warping3' in options.suffix:
warped_mask_loss = l1LossMask(warped_mask, (input_pair[c]['segmentation'] >= 0).float(), valid_mask)
losses += [warped_mask_loss]
pass
if 'warping2' in options.suffix or 'warping3' in options.suffix:
warped_image_loss = l1NormLossMask(warped_images, input_pair[c]['image'], dim=1, valid_mask=valid_mask)
losses += [warped_image_loss]
pass
input_pair[c]['warped_depth'] = (warped_depth * valid_mask + (1 - valid_mask) * 10).squeeze()
continue
loss = sum(losses)
losses = [l.data.item() for l in losses]
print("loss printed here")
epoch_losses.append(losses)
status = str(epoch + 1) + ' loss: '
for l in losses:
status += '%0.5f '%l
continue
sys.stdout.write('\r ' + str(sampleIndex) + ' ' + status)
sys.stdout.flush()
print("end--write of loss")
data_iterator.set_description(status)
loss.backward()
if (sampleIndex + 1) % options.batchSize == 0:
optimizer.step()
optimizer.zero_grad()
pass
if sampleIndex % 500 < options.batchSize or options.visualizeMode == 'debug':
## Visualize intermediate results
visualizeBatchPair(options, config, input_pair, detection_pair, indexOffset=sampleIndex % 500)
if (len(detection_pair[0]['detection']) > 0 and len(detection_pair[0]['detection']) < 30) and 'refine' in options.suffix:
visualizeBatchRefinement(options, config, input_pair[0], [{'mask': masks_gt, 'plane': planes_gt}, ] + results, indexOffset=sampleIndex % 500, concise=True)
pass
if options.visualizeMode == 'debug' and sampleIndex % 500 >= options.batchSize - 1:
exit(1)
pass
pass
if (sampleIndex + 1) % options.numTrainingImages == 0:
## Save models
print('loss', np.array(epoch_losses).mean(0))
torch.save(model.state_dict(), options.checkpoint_dir + '/checkpoint.pth')
torch.save(refine_model.state_dict(), options.checkpoint_dir + '/checkpoint_refine.pth')
torch.save(optimizer.state_dict(), options.checkpoint_dir + '/optim.pth')
pass
continue
continue
return
if __name__ == '__main__':
args = parse_args()
args.keyname = 'planercnn'
args.keyname += '_' + args.anchorType
if args.dataset != '':
args.keyname += '_' + args.dataset
pass
if args.trainingMode != 'all':
args.keyname += '_' + args.trainingMode
pass
if args.suffix != '':
args.keyname += '_' + args.suffix
pass
args.checkpoint_dir = 'checkpoint/' + args.keyname
args.test_dir = 'test/' + args.keyname
if False:
writeHTML(args.test_dir, ['image_0', 'segmentation_0', 'depth_0', 'depth_0_detection', 'depth_0_detection_ori'], labels=['input', 'segmentation', 'gt', 'before', 'after'], numImages=20, image_width=160, convertToImage=True)
exit(1)
os.system('rm ' + args.test_dir + '/*.png')
print('keyname=%s task=%s started'%(args.keyname, args.task))
train(args)