diff --git a/rag_depth/logs/checkpoint_task0.ckpt b/rag_depth/logs/checkpoint_task0.ckpt new file mode 100644 index 0000000..454ae5c Binary files /dev/null and b/rag_depth/logs/checkpoint_task0.ckpt differ diff --git a/rag_depth/logs/checkpoint_task1.ckpt b/rag_depth/logs/checkpoint_task1.ckpt new file mode 100644 index 0000000..cfb4c85 Binary files /dev/null and b/rag_depth/logs/checkpoint_task1.ckpt differ diff --git a/rag_depth/logs/checkpoint_task2.ckpt b/rag_depth/logs/checkpoint_task2.ckpt new file mode 100644 index 0000000..767abf9 Binary files /dev/null and b/rag_depth/logs/checkpoint_task2.ckpt differ diff --git a/rag_depth/logs/checkpoint_task3.ckpt b/rag_depth/logs/checkpoint_task3.ckpt new file mode 100644 index 0000000..1ed3b99 Binary files /dev/null and b/rag_depth/logs/checkpoint_task3.ckpt differ diff --git a/rag_depth/run_rag_depth.sh b/rag_depth/run_rag_depth.sh new file mode 100644 index 0000000..54c62aa --- /dev/null +++ b/rag_depth/run_rag_depth.sh @@ -0,0 +1,19 @@ +cd src +python run.py --mode=search \ + --device=0 \ + --id=sup-depth \ + --o_size=10 \ + --c_epochs=100 \ + --c_batch=16 \ + --c_lr=0.002 \ + --c_lr_a=0.01 \ + --c_lamb=0.0003 \ + --o_epochs=100 \ + --o_batch=12 \ + --o_lr=0.001 \ + --o_lr_a=0.01 \ + --o_lamb=0.0003 \ + --epochs=400 \ + --batch=8 \ + --lr=0.001 \ + --lamb=0.003 \ diff --git a/rag_depth/src/approaches/__init__.py b/rag_depth/src/approaches/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rag_depth/src/approaches/rag.py b/rag_depth/src/approaches/rag.py new file mode 100644 index 0000000..9b9d8c6 --- /dev/null +++ b/rag_depth/src/approaches/rag.py @@ -0,0 +1,536 @@ +import numpy as np +import torch +import torch.nn.functional as F +import torch.nn as nn + +import utils + +from copy import deepcopy + +from automl.mdenas_search import AutoSearch +from models.rag_model import Network +from automl.darts_genotypes import mdenas_pmnist, mdenas_mixture + +from automl.genotypes_2d import Genotype + +from utilstool.metrics import D1_metric, Thres_metric, EPE_metric +from utilstool.experiment import * + +def compute_errors(gt, pred): + thresh = np.maximum((gt / pred), (pred / gt)) + d1 = (thresh < 1.25).mean() + d2 = (thresh < 1.25 ** 2).mean() + d3 = (thresh < 1.25 ** 3).mean() + + rms = (gt - pred) ** 2 + rms = np.sqrt(rms.mean()) + + log_rms = (np.log(gt) - np.log(pred)) ** 2 + log_rms = np.sqrt(log_rms.mean()) + + abs_rel = np.mean(np.abs(gt - pred) / gt) + sq_rel = np.mean(((gt - pred) ** 2) / gt) + + err = np.log(pred) - np.log(gt) + silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100 + + err = np.abs(np.log10(pred) - np.log10(gt)) + log10 = np.mean(err) + + return [silog, abs_rel, log10, rms, sq_rel, log_rms, d1, d2, d3] + + +class Appr(object): + def __init__(self, clipgrad=5, writer=None, exp_name="None", device='cuda', args=None): + # the number of cells + self.model = None + self.archis = [] + # the device and tensorboard writer for training + self.device = device + self.writer = writer + self.exp_name = exp_name + + self.clipgrad = clipgrad + + self.args = args + + if args.mode == 'search': + # mode: search the best hyper-parameter + # the hyper parameters in cell search stage + self.c_epochs = args.c_epochs + self.c_batch = args.c_batch + self.c_lr = args.c_lr + self.c_lr_a = args.c_lr_a + self.c_lamb = args.c_lamb + # the hyper parameters in operation search stage + self.o_epochs = args.o_epochs + self.o_batch = args.o_batch + self.o_lr = args.o_lr + self.o_lr_a = args.o_lr_a + self.o_lamb = args.o_lamb + self.o_size = args.o_size + # the hyper parameters in training stage + self.epochs = args.epochs + self.batch = args.batch + self.lr = args.lr + self.lamb = args.lamb + + #import pdb; pdb.set_trace() + + # define the search method + self.auto_ml = AutoSearch(device=self.device, writer=self.writer, exp_name=self.exp_name, args=args) + # define optimizer and loss function + self.max_disp = 192 + self.optimizer = None + self.optimizer_o = None + + self.silog_criterion = silog_loss() + + def _get_optimizer(self, lr): + # optimizer to train the model parameters + if lr is None: + lr = self.lr + + return torch.optim.SGD(filter(lambda p: p.requires_grad, self.model.parameters()), + lr=lr, weight_decay=self.lamb, momentum=0.9) + + def _get_optimizer_o(self, lr=None): + if lr is None: + lr = self.o_lr + params = self.model.get_param(self.model.new_models) + + return torch.optim.SGD(params=params, lr=lr, momentum=0.9, weight_decay=self.o_lamb) + + def train(self, t, train_data, valid_data, device='cuda'): + # training network for task t + # 1 search cell for task t + #import pdb; pdb.set_trace() + genotype = self.search_cell(t, train_data, valid_data, self.c_batch, self.c_epochs, device=device) + # 2 search operation for task t > 0 + if t > 0: + # 2.1 expand + self.model.expand(t, genotype, device) + # 2.2 freeze the model + utils.freeze_model(self.model) + self.model.modify_param(self.model.new_models, True) + # 1.2.3 search the best expand action, the best action, and the best architecture + self.search_t(t, train_data, valid_data, self.o_batch, self.o_epochs, device=device) + best_archi = self.model.select(t) + print("best_archi is {}".format(best_archi)) + self.writer.add_text("Archi for task {}".format(t), + "{}".format(best_archi)) + self.archis.append(best_archi) + self.writer.add_text("ModelSize/Task_{}".format(t), + "model size = {}".format(utils.get_model_size(self.model))) + # 1.2.4 unfreeze the model that need to train + utils.freeze_model(self.model) + self.model.modify_param(self.model.model_to_train, True) + + # 3 training model for task t + self.train_t(t, train_data, valid_data, self.batch, self.epochs, device) + + def train_t(self, t, train_data, valid_data, batch_size, epochs, device='cuda'): + # training model for task t + # 0 prepare + print("Training stage of task {}".format(t)) + best_loss = np.inf + best_model = utils.get_model(self.model) + + lr = self.lr + self.optimizer = self._get_optimizer(lr) + # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=self.lr_patience, + # factor=self.lr_factor, threshold=0.001) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, epochs) + # 2 define the dataloader + train_loader = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True) + valid_loader = torch.utils.data.DataLoader( + valid_data, batch_size=batch_size, shuffle=False, num_workers=4, pin_memory=True) + # 3 training the model + for e in range(epochs): + # 3.1 train + self.train_epoch(t, train_loader, device=device) + # 3.2 compute training loss + train_loss, train_abs, train_log10, train_rmse, train_a1, train_a2, train_a3 = self.eval(t, train_loader, mode='train', device=device) + # 3.3 compute valid loss + valid_loss, valid_abs, valid_log10, valid_rmse, valid_a1, valid_a2, valid_a3 = self.eval(t, valid_loader, mode='train', device=device) + # 3.4 logging + print('| Epoch {:3d} | Train: loss={:.3f}, abs_rel={:.3f}, log10={:.3f}, rmse={:.3f}, delta1={:.3f}, delta2={:.3f}, delta3={:.3f} | \ + Valid: loss={:.3f}, abs_rel={:.3f}, log10={:.3f}, rmse={:.3f}, delta1={:.3f}, delta2={:.3f}, delta3={:.3f} |'.format( + e, train_loss, train_abs, train_log10, train_rmse, train_a1, train_a2, train_a3, \ + valid_loss, valid_abs, valid_log10, valid_rmse, valid_a1, valid_a2, valid_a3)) + self.writer.add_scalars('Train_Loss/Task: {}'.format(t), + {'train_loss': train_loss, 'valid_loss': valid_loss}, + global_step=e) + self.writer.add_scalars('Train_Abs_Rel/Task: {}'.format(t), + {'train_abs_rel': train_abs, 'valid_abs_rel': valid_abs}, + global_step=e) + self.writer.add_scalars('Train_RMSE/Task: {}'.format(t), + {'train_rmse': train_rmse, 'valid_rmse': valid_rmse}, + global_step=e) + + # 3.5 Adapt learning rate + scheduler.step(valid_loss) + # 3.6 update the best model + if valid_loss < best_loss: + best_loss = valid_loss + best_model = utils.get_model(self.model) + # 4 Restore best model + utils.set_model_(self.model, best_model) + return + + def train_epoch(self, t, train_loader, device='cuda'): + self.model.train() + #import pdb; pdb.set_trace() + # set tht mode of models which are reused (BN) + if t > 0: + + for i in range(self.model.length['stem_2d0']): + if i not in self.model.model_to_train['stem_2d0']: + self.model.stem2d0[i].eval() + for i in range(self.model.length['stem_2d1']): + if i not in self.model.model_to_train['stem_2d1']: + self.model.stem2d1[i].eval() + for i in range(self.model.length['stem_2d2']): + if i not in self.model.model_to_train['stem_2d2']: + self.model.stem2d2[i].eval() + for i in range(self.model.length['last_3_2d']): + if i not in self.model.model_to_train['last_3_2d']: + self.model.last_3_2d[i].eval() + for i in range(len(self.model.cells_2d)): + for k in range(self.model.length['cell_2d' + str(i)]): + if k not in self.model.model_to_train['cell_2d' + str(i)]: + self.model.cells_2d[i][k].eval() + + + for i in range(self.model.length['stem_3d0']): + if i not in self.model.model_to_train['stem_3d0']: + self.model.stem3d0[i].eval() + for i in range(self.model.length['stem_3d1']): + if i not in self.model.model_to_train['stem_3d1']: + self.model.stem3d1[i].eval() + + for i in range(len(self.model.cells_3d)): + for k in range(self.model.length['cell_3d' + str(i)]): + if k not in self.model.model_to_train['cell_3d' + str(i)]: + self.model.cells_3d[i][k].eval() + + + for i in range(self.model.length['last_3_3d']): + if i not in self.model.model_to_train['last_3_3d']: + self.model.last_3_3d[i].eval() + for i in range(self.model.length['last_6_3d']): + if i not in self.model.model_to_train['last_6_3d']: + self.model.last_6_3d[i].eval() + for i in range(self.model.length['last_12_3d']): + if i not in self.model.model_to_train['last_12_3d']: + self.model.last_12_3d[i].eval() + + + # Loop batch + flag = 1 + for sample in train_loader: + imgL, imgR, depth_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + # forward + depth_est = self.model.forward(imgL, imgR, t, self.archis[t]) + # todo test + mask = depth_gt > 0 + loss = self.silog_criterion.forward(depth_est, depth_gt, mask.to(torch.bool)) + # backward + self.optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clipgrad) + self.optimizer.step() + flag += 1 + #if flag > 20: + # break + + def search_cell(self, t, train_data, valid_data, batch_size, nepochs, device): + + print("Search cell for task {}".format(t)) + self.auto_ml = AutoSearch(device=self.device, writer=self.writer, exp_name=self.exp_name, args=self.args) + genotype = deepcopy(self.auto_ml.search(t, train_data, valid_data, batch_size, nepochs)) + + if t == 0: + self.model = Network(genotype, device).to(device) + self.archis.append(self.model.arch_init) + + self.writer.add_text("Task_0/genotype", + "genotype = {}".format(genotype), + global_step=0) + self.writer.add_text("ModelSize/Task_0", + "model size = {}".format(utils.get_model_size(self.model))) + return genotype + + def search_t(self, t, train_data, valid_data, batch_size, epochs, device): + # search operations for task t(t>0) + # 0 prepare + + print("Search Stage of task {}".format(t)) + best_loss = np.inf + best_model = utils.get_model(self.model) + lr = self.o_lr + # 1 define optimizers and scheduler + self.optimizer_o = self._get_optimizer_o(lr) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer_o, epochs, eta_min=0.001) + # 2 define the dataloader + num_train = len(train_data) + indices = list(range(num_train)) + split = int(np.floor(0.5 * num_train)) + train_loader = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), + num_workers=4, pin_memory=True) + valid_loader = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]), + num_workers=4, pin_memory=True) + + h_e = [torch.full(pro.size(), 0, dtype=torch.long) for pro in self.model.p] + h_a = [torch.full(pro.size(), 0.0, dtype=torch.float) for pro in self.model.p] + + for k in range(len(h_e)): + h_e[k][0:-1] = self.o_size + + # 3 search the best model architecture + for e in range(epochs): + # 3.0 prepare + for k, pro in enumerate(self.model.p): + self.writer.add_histogram('Archi_task_{}/{}'.format(t, k), + pro, global_step=e) + # 3.1 sample + selected_ops = [torch.multinomial(pro, 1).item() for pro in self.model.p] + # print("Selected ops: {}".format(selected_ops)) + + model_size = 0.0 + for i, idx in enumerate(selected_ops): + model_size += (idx == t) + if model_size < 1: + model_size = 1 + model_size = 9 / model_size + + self.writer.add_text("Selected ops for task {}".format(t), + "{}".format(selected_ops)) + + # 3.2 train + train_loss, train_abs, train_rmse = self.search_epoch(t, train_loader, batch_size, selected_ops, device) + valid_loss, valid_abs, valid_rmse, valid_a1 = self.search_eval(t, valid_loader, batch_size, selected_ops, device) + # logging + print('| Epoch {:3d} | Train: loss={:.3f}, abs_rel={:.3f}%, rmse={:.3f} | Valid: loss={:.3f}, abs_rel={:.3f}%, rmse={:.3f} |'.format( + e, train_loss, train_abs, train_rmse, valid_loss, valid_abs, valid_rmse)) + self.writer.add_scalars('Search_Loss/Task: {}'.format(t), + {'train_loss': train_loss, 'valid_loss': valid_loss}, + global_step=e) + self.writer.add_scalars('Search_Abs_Rel/Task: {}'.format(t), + {'train_abs_rel': train_abs, 'valid_abs_rel': valid_abs}, + global_step=e) + self.writer.add_scalars('Search_RMSE/Task: {}'.format(t), + {'train_rmse': train_rmse, 'valid_rmse': valid_rmse}, + global_step=e) + # 3.3 update h_e and h_a + for i, idx in enumerate(selected_ops): + h_e[i][idx] += 1 + #h_a[i][idx] = 1 - valid_d1 # base + h_a[i][idx] = np.sqrt(valid_a1) * np.log(model_size+1) / np.exp(1) + + # print("layer {}".format(i).center(50, "*")) + # print("h_e in layer {}".format(i).center(50, "*")) + # print(h_e[i]) + # print("h_a in layer {}".format(i).center(50, "*")) + # print(h_a[i]) + + # 3.4 update the probability + for k in range(len(self.model.p)): + dh_e_k = torch.reshape(h_e[k], (1, -1)) - torch.reshape(h_e[k], (-1, 1)) + dh_a_k = torch.reshape(h_a[k], (1, -1)) - torch.reshape(h_a[k], (-1, 1)) + + # modify + # vector1 = torch.sum((dh_e_k < 0) * (dh_a_k > 0), dim=1) + vector1 = torch.sum((dh_e_k < 0) * (dh_a_k > 0), dim=0) + # vector1[-1] /= self.o_size + # print("vector1: {}".format(vector1)) + # vector2 = torch.sum((dh_e_k > 0) * (dh_a_k < 0), dim=1) + vector2 = torch.sum((dh_e_k > 0) * (dh_a_k < 0), dim=0) + # print("vector2: {}".format(vector2)) + update = (vector1 - vector2).float() + # update[-1] /= self.o_size + self.model.p[k] += (self.o_lr_a * update) + self.model.p[k] = F.softmax(self.model.p[k]) + + # 3.5 Adapt learning rate + scheduler.step() + # 3.6 update the best model + if valid_loss < best_loss: + best_loss = valid_loss + best_model = utils.get_model(self.model) + + # 4 Restore best model + utils.set_model_(self.model, best_model) + return + + def search_epoch(self, t, train_loader, batch_size, selected_ops, device='cuda'): + self.model.train() + # set tht mode of models which are reused (BN) + for i in range(self.model.length['stem_2d0']): + self.model.stem2d0[i].eval() + for i in range(self.model.length['stem_2d1']): + self.model.stem2d1[i].eval() + for i in range(self.model.length['stem_2d2']): + self.model.stem2d2[i].eval() + for i in range(self.model.length['last_3_2d']): + self.model.last_3_2d[i].eval() + for i in range(self.model.length['stem_3d0']): + self.model.stem3d0[i].eval() + for i in range(self.model.length['stem_3d1']): + self.model.stem3d1[i].eval() + + + for i in range(len(self.model.cells_2d)): + for k in range(self.model.length['cell_2d' + str(i)]): + self.model.cells_2d[i][k].eval() + + for i in range(len(self.model.cells_3d)): + for k in range(self.model.length['cell_3d' + str(i)]): + self.model.cells_3d[i][k].eval() + + # 2 Loop batches + avg_train_scalars = AverageMeterDict() + abs_rel_sum = 0 + rmse_sum = 0 + count = 0 + for step, sample in enumerate(train_loader): + imgL, imgR, depth_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + length = imgL.size()[0] + # 2.2.3 Forward current model + depth_est = self.model.search_forward(imgL, imgR, t, selected_ops) + # todo test + mask = depth_gt > 0 + loss = self.silog_criterion.forward(depth_est, depth_gt, mask.to(torch.bool)) + + # 2.2.4 Backward + self.optimizer_o.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.clipgrad) + self.optimizer_o.step() + + with torch.no_grad(): + scalar_outputs = {"loss": loss} + scalar_outputs = tensor2float(scalar_outputs) + avg_train_scalars.update(scalar_outputs) + + depth_gt = depth_gt.cpu().numpy() + depth_est = depth_est.cpu().numpy() + mask = depth_gt > 0 + depth_errors = compute_errors(depth_gt[mask], depth_est[mask]) + abs_rel_sum += depth_errors[1] + rmse_sum += depth_errors[3] + count += 1 + + #if step > 20: + # break + + avg_train_scalars = avg_train_scalars.mean() + loss_avg = avg_train_scalars["loss"] + + abs_rel_avg = abs_rel_sum / count + rmse_avg = rmse_sum / count + + return loss_avg, abs_rel_avg, rmse_avg + + def eval(self, t, test_loader, mode, device): + avg_eval_scalars = AverageMeterDict() + abs_rel_sum = 0 + rmse_sum = 0 + log10_sum = 0 + a1_sum = 0 + a2_sum = 0 + a3_sum = 0 + count = 0 + self.model.eval() + current_task = t + with torch.no_grad(): + for step, sample in enumerate(test_loader): + imgL, imgR, depth_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + # forward + depth_est = self.model.forward(imgL, imgR, t, self.archis[t]) + # compute loss + mask = depth_gt > 0 + loss = self.silog_criterion.forward(depth_est, depth_gt, mask.to(torch.bool)) + + scalar_outputs = {"loss": loss} + scalar_outputs = tensor2float(scalar_outputs) + avg_eval_scalars.update(scalar_outputs) + + depth_gt = depth_gt.cpu().numpy() + depth_est = depth_est.cpu().numpy() + mask = depth_gt > 0 + depth_errors = compute_errors(depth_gt[mask], depth_est[mask]) + abs_rel_sum += depth_errors[1] + log10_sum += depth_errors[2] + rmse_sum += depth_errors[3] + a1_sum += depth_errors[6] + a2_sum += depth_errors[7] + a3_sum += depth_errors[8] + count += 1 + + #if step > 20: + # break + + avg_eval_scalars = avg_eval_scalars.mean() + loss_avg = avg_eval_scalars["loss"] + + abs_rel_avg = abs_rel_sum / count + log10_avg = log10_sum / count + rmse_avg = rmse_sum / count + a1_avg = a1_sum / count + a2_avg = a2_sum / count + a3_avg = a3_sum / count + + + return loss_avg, abs_rel_avg, log10_avg, rmse_avg, a1_avg, a2_avg, a3_avg + + def search_eval(self, t, test_loader, batch_size, selected_ops, device): + avg_eval_scalars = AverageMeterDict() + abs_rel_sum = 0 + rmse_sum = 0 + a1_sum = 0 + count = 0 + self.model.eval() + + with torch.no_grad(): + for step, sample in enumerate(test_loader): + imgL, imgR, depth_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + length = imgL.size()[0] + # forward + depth_est = self.model.search_forward(imgL, imgR, t, selected_ops) + # compute loss + mask = depth_gt > 0 + loss = self.silog_criterion.forward(depth_est, depth_gt, mask.to(torch.bool)) + #loss = F.smooth_l1_loss(disp_est[mask], disp_gt[mask], size_average=True) + + scalar_outputs = {"loss": loss} + scalar_outputs = tensor2float(scalar_outputs) + avg_eval_scalars.update(scalar_outputs) + + depth_gt = depth_gt.cpu().numpy() + depth_est = depth_est.cpu().numpy() + mask = depth_gt > 0 + depth_errors = compute_errors(depth_gt[mask], depth_est[mask]) + abs_rel_sum += depth_errors[1] + rmse_sum += depth_errors[3] + a1_sum += depth_errors[6] + count += 1 + + + #if step > 20: + # break + + + avg_eval_scalars = avg_eval_scalars.mean() + loss_avg = avg_eval_scalars["loss"] + + abs_rel_avg = abs_rel_sum / count + rmse_avg = rmse_sum / count + a1_avg = a1_sum / count + + return loss_avg, abs_rel_avg, rmse_avg, a1_avg diff --git a/rag_depth/src/automl/__init__.py b/rag_depth/src/automl/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rag_depth/src/automl/build_model_2d.py b/rag_depth/src/automl/build_model_2d.py new file mode 100644 index 0000000..69f835c --- /dev/null +++ b/rag_depth/src/automl/build_model_2d.py @@ -0,0 +1,245 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from automl.genotypes_2d import PRIMITIVES +from automl.genotypes_2d import Genotype +from automl.operations_2d import * +import torch.nn.functional as F +import numpy as np +import pdb + +class MixedOp(nn.Module): + + def __init__(self, C, stride): + super(MixedOp, self).__init__() + self._ops = nn.ModuleList() + for primitive in PRIMITIVES: + op = OPS_2d[primitive](C, stride) + if 'pool' in primitive: + op = nn.Sequential(op, nn.BatchNorm2d(C)) + self._ops.append(op) + + def forward(self, x, selected_op): + return self._ops[selected_op](x) + + +class Cell(nn.Module): + + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_fmultiplier_down, prev_fmultiplier_same, prev_fmultiplier_up, + filter_multiplier): + + super(Cell, self).__init__() + + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + + self.C_prev_prev = int(prev_prev_fmultiplier * block_multiplier) + self._prev_fmultiplier_same = prev_fmultiplier_same + + if prev_fmultiplier_down is not None: + self.C_prev_down = int(prev_fmultiplier_down * block_multiplier) + self.preprocess_down = ConvBR_2d( + self.C_prev_down, self.C_out, 1, 1, 0) + if prev_fmultiplier_same is not None: + self.C_prev_same = int(prev_fmultiplier_same * block_multiplier) + self.preprocess_same = ConvBR_2d( + self.C_prev_same, self.C_out, 1, 1, 0) + if prev_fmultiplier_up is not None: + self.C_prev_up = int(prev_fmultiplier_up * block_multiplier) + self.preprocess_up = ConvBR_2d( + self.C_prev_up, self.C_out, 1, 1, 0) + + if prev_prev_fmultiplier != -1: + self.pre_preprocess = ConvBR_2d( + self.C_prev_prev, self.C_out, 1, 1, 0) + + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + + for i in range(self._steps): + for j in range(2 + i): + stride = 1 + if prev_prev_fmultiplier == -1 and j == 0: + op = None + else: + op = MixedOp(self.C_out, stride) + self._ops.append(op) + + self._initialize_weights() + + def scale_dimension(self, dim, scale): + assert isinstance(dim, int) + return int((float(dim) - 1.0) * scale + 1.0) if dim % 2 else int(dim * scale) + + def prev_feature_resize(self, prev_feature, mode): + if mode == 'down': + feature_size_h = self.scale_dimension(prev_feature.shape[2], 0.5) + feature_size_w = self.scale_dimension(prev_feature.shape[3], 0.5) + elif mode == 'up': + feature_size_h = self.scale_dimension(prev_feature.shape[2], 2) + feature_size_w = self.scale_dimension(prev_feature.shape[3], 2) + + return F.interpolate(prev_feature, (feature_size_h, feature_size_w), mode='bilinear', align_corners=True) + + def forward(self, s0, s1_down, s1_same, s1_up, n_alphas): + + if s1_down is not None: + s1_down = self.prev_feature_resize(s1_down, 'down') + s1_down = self.preprocess_down(s1_down) + size_h, size_w = s1_down.shape[2], s1_down.shape[3] + if s1_same is not None: + s1_same = self.preprocess_same(s1_same) + size_h, size_w = s1_same.shape[2], s1_same.shape[3] + if s1_up is not None: + s1_up = self.prev_feature_resize(s1_up, 'up') + s1_up = self.preprocess_up(s1_up) + size_h, size_w = s1_up.shape[2], s1_up.shape[3] + all_states = [] + if s0 is not None: + + s0 = F.interpolate(s0, (size_h, size_w), mode='bilinear', align_corners=True) if (s0.shape[2] != size_h) or (s0.shape[3] != size_w) else s0 + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + if s1_down is not None: + states_down = [s0, s1_down] + all_states.append(states_down) + if s1_same is not None: + states_same = [s0, s1_same] + all_states.append(states_same) + if s1_up is not None: + states_up = [s0, s1_up] + all_states.append(states_up) + else: + if s1_down is not None: + states_down = [0, s1_down] + all_states.append(states_down) + if s1_same is not None: + states_same = [0, s1_same] + all_states.append(states_same) + if s1_up is not None: + states_up = [0, s1_up] + all_states.append(states_up) + + final_concates = [] + for states in all_states: + offset = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if self._ops[branch_index] is None: + continue + new_state = self._ops[branch_index](h, n_alphas[branch_index]) + new_states.append(new_state) + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + final_concates.append(concat_feature) + return final_concates + + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class AutoFeature(nn.Module): + def __init__(self, num_layers=4, filter_multiplier=4, block_multiplier=3, step=3, cell=Cell): + super(AutoFeature, self).__init__() + + self.cells = nn.ModuleList() + self._num_layers = num_layers + self._step = step + self._block_multiplier = block_multiplier + self._filter_multiplier = filter_multiplier + + f_initial = int(self._filter_multiplier) + half_f_initial = int(f_initial / 2) + self._num_end = f_initial * self._block_multiplier + + self.stem0 = ConvBR_2d(3, half_f_initial * self._block_multiplier, 3, stride=1, padding=1) + self.stem1 = ConvBR_2d(half_f_initial * self._block_multiplier, half_f_initial * self._block_multiplier, 3, stride=3, padding=1) + self.stem2 = ConvBR_2d(half_f_initial * self._block_multiplier, f_initial * self._block_multiplier, 3, stride=1, padding=1) + + # network architecture [1, 0, 1, 0], 4 layers + for i in range(self._num_layers): + if i == 0: + _cell = cell(self._step, self._block_multiplier, -1, + f_initial, None, None, + self._filter_multiplier * 2) + elif i == 1: + _cell = cell(self._step, self._block_multiplier, f_initial, + None, self._filter_multiplier, self._filter_multiplier * 2, + self._filter_multiplier) + elif i == 2: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, + self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, + self._filter_multiplier * 2) + elif i == 3: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier, + None, self._filter_multiplier, self._filter_multiplier * 2, + self._filter_multiplier) + + self.cells += [_cell] + + self.last_3 = ConvBR_2d(self._num_end , self._num_end, 1, 1, 0, bn=False, relu=False) + self.last_6 = ConvBR_2d(self._num_end*2 , self._num_end, 1, 1, 0) + self.last_12 = ConvBR_2d(self._num_end*4 , self._num_end*2, 1, 1, 0) + self.last_24 = ConvBR_2d(self._num_end*8 , self._num_end*4, 1, 1, 0) + + def forward(self, x, n_alphas): + + stem0 = self.stem0(x) + stem1 = self.stem1(stem0) + stem2 = self.stem2(stem1) + + for layer in range(self._num_layers): + + if layer == 0: + level6_new, = self.cells[layer](None, stem2, None, None, n_alphas) # [1, 64, 32, 64] + + elif layer == 1: + level3_new_1, = self.cells[layer](stem2, None, None, level6_new, n_alphas) # [1, 32, 64, 128] + + elif layer == 2: + level6_new_1, = self.cells[layer](level6_new, level3_new_1, None, None, n_alphas) # [1, 64, 32, 64] + + elif layer == 3: + level3_new_2, = self.cells[layer](level3_new_1, None, None, level6_new_1, n_alphas) # [1, 32, 64, 128] + + last_output = level3_new_2 + + h, w = stem2.size()[2], stem2.size()[3] + upsample_6 = nn.Upsample(size=stem2.size()[2:], mode='bilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[h//2, w//2], mode='bilinear', align_corners=True) + upsample_24 = nn.Upsample(size=[h//4, w//4], mode='bilinear', align_corners=True) + + if last_output.size()[2] == h: + fea = self.last_3(last_output) + elif last_output.size()[2] == h//2: + fea = self.last_3(upsample_6(self.last_6(last_output))) + elif last_output.size()[2] == h//4: + fea = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(last_output))))) + elif last_output.size()[2] == h//8: + fea = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(upsample_24(self.last_24(last_output))))))) + + return fea + + def get_params(self): + bn_params = [] + non_bn_params = [] + for name, param in self.named_parameters(): + if 'bn' in name or 'downsample.1' in name: + bn_params.append(param) + else: + bn_params.append(param) + return bn_params, non_bn_params \ No newline at end of file diff --git a/rag_depth/src/automl/build_model_3d.py b/rag_depth/src/automl/build_model_3d.py new file mode 100644 index 0000000..6316554 --- /dev/null +++ b/rag_depth/src/automl/build_model_3d.py @@ -0,0 +1,276 @@ +import torch +import torch.nn as nn +import numpy as np +#from automl.genotypes_3d import PRIMITIVES_3D +#from automl.genotypes_3d import Genotype_3D +#from automl.operations_3d import * +from automl.genotypes_2d import PRIMITIVES +from automl.genotypes_2d import Genotype +from automl.operations_2d import * +import torch.nn.functional as F +import numpy as np +import pdb + +class MixedOp(nn.Module): + + def __init__(self, C, stride): + super(MixedOp, self).__init__() + self._ops = nn.ModuleList() + for primitive in PRIMITIVES: + op = OPS_2d[primitive](C, stride) + if 'pool' in primitive: + op = nn.Sequential(op, nn.BatchNorm2d(C)) + self._ops.append(op) + + def forward(self, x, selected_op): + return self._ops[selected_op](x) + + +class Cell(nn.Module): + + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_fmultiplier_down, prev_fmultiplier_same, prev_fmultiplier_up, + filter_multiplier): + + super(Cell, self).__init__() + + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + + self.C_prev_prev = int(prev_prev_fmultiplier * block_multiplier) + self._prev_fmultiplier_same = prev_fmultiplier_same + + if prev_fmultiplier_down is not None: + self.C_prev_down = int(prev_fmultiplier_down * block_multiplier) + self.preprocess_down = ConvBR_2d( + self.C_prev_down, self.C_out, 1, 1, 0) + if prev_fmultiplier_same is not None: + self.C_prev_same = int(prev_fmultiplier_same * block_multiplier) + self.preprocess_same = ConvBR_2d( + self.C_prev_same, self.C_out, 1, 1, 0) + if prev_fmultiplier_up is not None: + self.C_prev_up = int(prev_fmultiplier_up * block_multiplier) + self.preprocess_up = ConvBR_2d( + self.C_prev_up, self.C_out, 1, 1, 0) + + if prev_prev_fmultiplier != -1: + self.pre_preprocess = ConvBR_2d( + self.C_prev_prev, self.C_out, 1, 1, 0) + + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + + for i in range(self._steps): + for j in range(2 + i): + stride = 1 + if prev_prev_fmultiplier == -1 and j == 0: + op = None + else: + op = MixedOp(self.C_out, stride) + self._ops.append(op) + + self._initialize_weights() + + def scale_dimension(self, dim, scale): + assert isinstance(dim, int) + return int((float(dim) - 1.0) * scale + 1.0) if dim % 2 else int(dim * scale) + + def prev_feature_resize(self, prev_feature, mode): + if mode == 'down': + feature_size_h = self.scale_dimension(prev_feature.shape[2], 0.5) + feature_size_w = self.scale_dimension(prev_feature.shape[3], 0.5) + elif mode == 'up': + feature_size_h = self.scale_dimension(prev_feature.shape[2], 2) + feature_size_w = self.scale_dimension(prev_feature.shape[3], 2) + + return F.interpolate(prev_feature, (feature_size_h, feature_size_w), mode='bilinear', align_corners=True) + + def forward(self, s0, s1_down, s1_same, s1_up, n_alphas): + + if s1_down is not None: + s1_down = self.prev_feature_resize(s1_down, 'down') + s1_down = self.preprocess_down(s1_down) + size_h, size_w = s1_down.shape[2], s1_down.shape[3] + if s1_same is not None: + s1_same = self.preprocess_same(s1_same) + size_h, size_w = s1_same.shape[2], s1_same.shape[3] + if s1_up is not None: + s1_up = self.prev_feature_resize(s1_up, 'up') + s1_up = self.preprocess_up(s1_up) + size_h, size_w = s1_up.shape[2], s1_up.shape[3] + all_states = [] + if s0 is not None: + + s0 = F.interpolate(s0, (size_h, size_w), mode='bilinear', align_corners=True) if (s0.shape[2] != size_h) or (s0.shape[3] != size_w) else s0 + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + if s1_down is not None: + states_down = [s0, s1_down] + all_states.append(states_down) + if s1_same is not None: + states_same = [s0, s1_same] + all_states.append(states_same) + if s1_up is not None: + states_up = [s0, s1_up] + all_states.append(states_up) + else: + if s1_down is not None: + states_down = [0, s1_down] + all_states.append(states_down) + if s1_same is not None: + states_same = [0, s1_same] + all_states.append(states_same) + if s1_up is not None: + states_up = [0, s1_up] + all_states.append(states_up) + + final_concates = [] + for states in all_states: + offset = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if self._ops[branch_index] is None: + continue + new_state = self._ops[branch_index](h, n_alphas[branch_index]) + new_states.append(new_state) + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + final_concates.append(concat_feature) + return final_concates + + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class AutoMatching(nn.Module): + def __init__(self, num_layers=8, filter_multiplier=4, block_multiplier=3, step=3, cell=Cell): + super(AutoMatching, self).__init__() + + self.cells = nn.ModuleList() + self._step = step + self._num_layers = num_layers + self._block_multiplier = block_multiplier + self._filter_multiplier = filter_multiplier + + f_initial = int(self._filter_multiplier) + self._num_end = f_initial * self._block_multiplier + + self.stem0 = ConvBR_2d(self._num_end, self._num_end, 3, stride=1, padding=1) + + # network architecture [0, 0, 0, 1, 2, 1, 2, 2], 8 layers + for i in range(self._num_layers): + + if i == 0: + _cell = cell(self._step, self._block_multiplier, -1, + None, f_initial, None, + self._filter_multiplier) + + elif i == 1: + _cell = cell(self._step, self._block_multiplier, f_initial, + None, self._filter_multiplier, self._filter_multiplier * 2, + self._filter_multiplier) + + elif i == 2: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier, + None, self._filter_multiplier, self._filter_multiplier * 2, + self._filter_multiplier) + + elif i == 3: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier, + self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, + self._filter_multiplier * 2) + elif i == 4: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier, + self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 8, + self._filter_multiplier * 4) + + elif i == 5: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, + self._filter_multiplier, self._filter_multiplier * 2, self._filter_multiplier * 4, + self._filter_multiplier * 2) + + elif i == 6: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier * 4, + self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 8, + self._filter_multiplier * 4) + + else: + _cell = cell(self._step, self._block_multiplier, self._filter_multiplier * 2, + self._filter_multiplier * 2, self._filter_multiplier * 4, self._filter_multiplier * 8, + self._filter_multiplier * 4) + + self.cells += [_cell] + + self.last_3 = ConvBR_2d(self._num_end, 1, 3, 1, 1, bn=False, relu=False) + self.last_6 = ConvBR_2d(self._num_end*2 , self._num_end, 1, 1, 0) + self.last_12 = ConvBR_2d(self._num_end*4 , self._num_end*2, 1, 1, 0) + self.last_24 = ConvBR_2d(self._num_end*8 , self._num_end*4, 1, 1, 0) + + + def forward(self, x, n_alphas): + self.level_3 = [] + self.level_6 = [] + self.level_12 = [] + self.level_24 = [] + + + stem = self.stem0(x) # [1, 12, 64, 64, 128] + + + # network architecture [0, 0, 0, 1, 2, 1, 2, 2], 8 layers + for layer in range(self._num_layers): + if layer == 0: + level3_new, = self.cells[layer](None, None, stem, None, n_alphas) # [1, 12, 64, 64, 128] + + elif layer == 1: + level3_new_1, = self.cells[layer](stem, None, level3_new, None, n_alphas) # [1, 12, 64, 64, 128] + + elif layer == 2: + level3_new_2, = self.cells[layer](level3_new, None, level3_new_1, None, n_alphas) # [1, 12, 64, 64, 128] + + elif layer == 3: + level6_new, = self.cells[layer](level3_new_1, level3_new_2, None, None, n_alphas) # [1, 24, 32, 32, 64] + + elif layer == 4: + level12_new, = self.cells[layer](level3_new_2, level6_new, None, None, n_alphas) # [1, 48, 16, 16, 32] + + elif layer == 5: + level6_new, = self.cells[layer](level6_new, None, None, level12_new, n_alphas) # [1, 24, 32, 32, 64] + + elif layer == 6: + level12_new_1, = self.cells[layer](level12_new, level6_new, None, None, n_alphas) # [1, 48, 16, 16, 32] + + elif layer == 7: + level12_new_2, = self.cells[layer](level6_new, None, level12_new_1, None, n_alphas) + + + last_output = level12_new_2 + + h, w = x.size()[2], x.size()[3] + upsample_6 = nn.Upsample(size=x.size()[2:], mode='bilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[h//2, w//2], mode='bilinear', align_corners=True) + upsample_24 = nn.Upsample(size=[h//4, w//4], mode='bilinear', align_corners=True) + + if last_output.size()[2] == h: + mat = self.last_3(last_output) + elif last_output.size()[2] == h//2: + mat = self.last_3(upsample_6(self.last_6(last_output))) + elif last_output.size()[2] == h//4: + mat = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(last_output))))) + elif last_output.size()[2] == h//8: + mat = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(upsample_24(self.last_24(last_output))))))) + + return mat + diff --git a/rag_depth/src/automl/darts_architecture.py b/rag_depth/src/automl/darts_architecture.py new file mode 100644 index 0000000..ca7c954 --- /dev/null +++ b/rag_depth/src/automl/darts_architecture.py @@ -0,0 +1,93 @@ +import torch +import numpy as np +import torch.nn as nn +from copy import deepcopy +from torch.autograd import Variable + + +def _concat(xs): + return torch.cat([x.view(-1) for x in xs]) + + +class Architect(object): + + def __init__(self, model, lr_a, momentum, weight_decay, weight_decay_a): + self.network_momentum = momentum + self.network_weight_decay = weight_decay + self.model = model + self.virtual_model = deepcopy(self.model) + + params = [] + for p in self.model.arch_parameters().values(): + params.append(p) + + self.optimizer = torch.optim.Adam(params=params, + lr=lr_a, betas=(0.5, 0.999), + weight_decay=weight_decay_a) + + def step(self, input_train, target_train, input_valid, target_valid, eta, network_optimizer, unrolled): + self.optimizer.zero_grad() + if unrolled: + self._backward_step_unrolled(input_train, target_train, input_valid, target_valid, eta, network_optimizer) + else: + self._backward_step(input_valid, target_valid) + self.optimizer.step() + + def _backward_step(self, input_valid, target_valid): + loss = self.model.loss(input_valid, target_valid) + loss.backward() + + def virtual_step(self, input_train, target_train, eta, network_optimizer): + loss = self.model.loss(input_train, target_train) + gradients = torch.autograd.grad(loss, self.model.parameters()) + + with torch.no_grad(): + for p, vp, g in zip(self.model.parameters(), self.virtual_model.parameters(), gradients): + m = network_optimizer.state[p].get('momentum_buffer', 0) * self.network_momentum + vp.copy_(p - eta * (m+g+self.network_weight_decay*p)) + + for a, va in zip(self.model.arch_parameters(), self.virtual_model.arch_parameters()): + va.copy_(a) + + def _backward_step_unrolled(self, input_train, target_train, input_valid, target_valid, eta, network_optimizer): + self.virtual_step(input_train, target_train, eta, network_optimizer) + loss = self.virtual_model.loss(input_valid, target_valid) + + # compute gradient + v_a = tuple(self.virtual_model.arch_parameters()) + v_p = tuple(self.virtual_model.parameters()) + v_grad = torch.autograd.grad(loss, v_a+v_p) + d_a = v_grad[:len(v_a)] + d_p = v_grad[len(v_a):] + + hessian = self._hessian_vector_product(d_p, input_train, target_train) + + with torch.no_grad(): + for a, d, h in zip(self.model.arch_parameters(), d_a, hessian): + a.grad = d - eta * h + + def _hessian_vector_product(self, dw, input_train, target_train, r=1e-2): + eps = r / torch.cat([w.view(-1) for w in dw]).norm() + + # w+ = w + eps * dw + with torch.no_grad(): + for p, d in zip(self.model.parameters(), dw): + p += eps * d + loss = self.model.loss(input_train, target_train) + da_pos = torch.autograd.grad(loss, self.model.arch_parameters()) + + # w- = w - eps * dw + with torch.no_grad(): + for p, d in zip(self.model.parameters(), dw): + p -= 2 * eps * d + loss = self.model.loss(input_train, target_train) + da_neg = torch.autograd.grad(loss, self.model.arch_parameters()) + + # recover w + with torch.no_grad(): + for p, d in zip(self.model.parameters(), dw): + p += eps * d + + hessian = [(p-n) / 2.0 * eps for p, n in zip(da_pos, da_neg)] + + return hessian diff --git a/rag_depth/src/automl/darts_basicmodel.py b/rag_depth/src/automl/darts_basicmodel.py new file mode 100644 index 0000000..dc58885 --- /dev/null +++ b/rag_depth/src/automl/darts_basicmodel.py @@ -0,0 +1,172 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from copy import deepcopy + +from automl.darts_genotypes import PRIMITIVES +from automl.darts_genotypes import Genotype +from automl.darts_operation import * + + +class MixedOp(nn.Module): + # mixed operation in every edge + def __init__(self, C, stride): + super(MixedOp, self).__init__() + self._ops = nn.ModuleList() + for primitive in PRIMITIVES: + op = OPS[primitive](C, stride, False) + if 'pool' in primitive: + op = nn.Sequential(op, nn.BatchNorm2d(C, affine=False)) + self._ops.append(op) + + def forward(self, x, weights): + return sum(w * op(x) for w, op in zip(weights, self._ops)) + + +class Cell(nn.Module): + + def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev): + super(Cell, self).__init__() + self.reduction = reduction + + if reduction_prev: + self.preprocess0 = FactorizedReduce(C_prev_prev, C, affine=False) + else: + self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=False) + self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=False) + self._steps = steps + self._multiplier = multiplier + + self._ops = nn.ModuleList() + self._bns = nn.ModuleList() + for i in range(self._steps): + for j in range(2 + i): + stride = 2 if reduction and j < 2 else 1 + op = MixedOp(C, stride) + self._ops.append(op) + + def forward(self, s0, s1, weights): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + offset = 0 + for i in range(self._steps): + # each intermediate node has multiple inputs + s = sum(self._ops[offset + j](h, weights[offset + j]) for j, h in enumerate(states)) + offset += len(states) + states.append(s) + + return torch.cat(states[-self._multiplier:], dim=1) + + +class BasicNetwork(nn.Module): + + def __init__(self, input_c, C, num_classes, layers, criterion, steps=4, multiplier=4, stem_multiplier=3, device='cuda:0'): + super(BasicNetwork, self).__init__() + self.input_c = input_c + self._C = C + self._num_classes = num_classes + self._layers = layers + self._criterion = criterion + self._steps = steps # the number of intermediate nodes + self._multiplier = multiplier + self.device = device + + C_curr = stem_multiplier * C + self.stem = nn.Sequential( + nn.Conv2d(self.input_c, C_curr, 3, padding=1, bias=False), + nn.BatchNorm2d(C_curr) + ) + + C_prev_prev, C_prev, C_curr = C_curr, C_curr, C + self.cells = nn.ModuleList() + reduction_prev = False + for i in range(layers): + if i in [layers // 3, 2 * layers // 3]: + C_curr *= 2 + reduction = True + else: + reduction = False + cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev) + reduction_prev = reduction + self.cells += [cell] + C_prev_prev, C_prev = C_prev, multiplier * C_curr + + self.global_pooling = nn.AdaptiveAvgPool2d(1) + self.classifier = nn.Linear(C_prev, num_classes) + + self._initialize_alphas() + + def new(self): + model_new = BasicNetwork(self.input_c, self._C, self._num_classes, self._layers, self._criterion).to(self.device) + model_new._arch_parameters = deepcopy(self.arch_parameters()) + + return model_new + + def forward(self, x): + s0 = s1 = self.stem(x) + for i, cell in enumerate(self.cells): + if cell.reduction: + weights = F.softmax(self._arch_parameters["alphas_reduce"], dim=-1) + else: + weights = F.softmax(self._arch_parameters["alphas_normal"], dim=-1) + s0, s1 = s1, cell(s0, s1, weights) + out = self.global_pooling(s1) + logits = self.classifier(out.view(out.size(0), -1)) + return logits + + def loss(self, x, y): + logits = self(x) + return self._criterion(logits, y) + + def _initialize_alphas(self): + k = sum(1 for i in range(self._steps) for n in range(2 + i)) + num_ops = len(PRIMITIVES) + + self._arch_parameters = nn.ParameterDict({ + "alphas_normal": nn.Parameter(1e-3 * torch.randn((k, num_ops), device=self.device, requires_grad=True)), + "alphas_reduce": nn.Parameter(1e-3 * torch.randn((k, num_ops), device=self.device, requires_grad=True)) + }) + + def arch_parameters(self): + return self._arch_parameters + + def genotype(self): + + def _parse(weights): + gene = [] + n = 2 + start = 0 + for i in range(self._steps): # for each intermediate node + # get the weights of the input edges of this intermediate node + end = start + n + w = weights[start:end].copy() + # get the sorted edges and select the top-2 + edges = sorted(range(i + 2), + key=lambda x: -max(w[x][k] for k in range(len(w[x])) if k != PRIMITIVES.index('none')))[ + :2] + for j in edges: # select the best operation for each edge + k_best = None + for k in range(len(w[j])): + if k != PRIMITIVES.index('none'): + if k_best is None or w[j][k] > w[j][k_best]: + k_best = k + # each intermediate node has two edges, the k_best is the operation of the edge and j is the input + # node of the edge + gene.append((PRIMITIVES[k_best], j)) + start = end + n += 1 + return gene + + gene_normal = _parse(F.softmax(self._arch_parameters["alphas_normal"], dim=-1).cpu().detach().numpy()) + gene_reduce = _parse(F.softmax(self._arch_parameters["alphas_reduce"], dim=-1).cpu().detach().numpy()) + + concat = range(2 + self._steps - self._multiplier, self._steps + 2) + genotype = Genotype( + normal=gene_normal, normal_concat=concat, + reduce=gene_reduce, reduce_concat=concat + ) + return genotype + diff --git a/rag_depth/src/automl/darts_genotypes.py b/rag_depth/src/automl/darts_genotypes.py new file mode 100644 index 0000000..4f93ddc --- /dev/null +++ b/rag_depth/src/automl/darts_genotypes.py @@ -0,0 +1,114 @@ +from collections import namedtuple + +Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat') + +PRIMITIVES = [ + 'none', + 'max_pool_3x3', + 'avg_pool_3x3', + 'skip_connect', + 'sep_conv_3x3', + 'sep_conv_5x5', + 'dil_conv_3x3', + 'dil_conv_5x5' +] + +# the architectures of origin paper +DARTS_V1 = Genotype( + # the normal cell: have 2 input nodes(0,1), 4 intermediate nodes(2,3,4,5) and 1 output node(6) + # for example: + # node 2 + # has 2 edges + # the first edge is connected with node 1, and operation is sep_conv_3x3 + # the second edge is connected with node 0, and operation is sep_conv_3x3 + # node 3 + # has 2 edges + # the first edge is connected with node 0, and operation is skip_connect + # the second edge is connected with node 1, and operation is sep_conv_3x3 + # ...... + normal=[('sep_conv_3x3', 1), ('sep_conv_3x3', 0), # edges of node 2 + ('skip_connect', 0), ('sep_conv_3x3', 1), # edges of node 3 + ('skip_connect', 0), ('sep_conv_3x3', 1), # edges of node 4 + ('sep_conv_3x3', 0), ('skip_connect', 2)], # edges of node 5 + # the outputs of node 2, 3, 4 and 5 are concatenated as the output of cell + normal_concat=[2, 3, 4, 5], + # the reduction cell + reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), + ('skip_connect', 2), ('max_pool_3x3', 0), + ('max_pool_3x3', 0), ('skip_connect', 2), + ('skip_connect', 2), ('avg_pool_3x3', 0)], + reduce_concat=[2, 3, 4, 5]) + +DARTS_V2 = Genotype( + normal=[('sep_conv_3x3', 0), ('sep_conv_3x3', 1), + ('sep_conv_3x3', 0), ('sep_conv_3x3', 1), + ('sep_conv_3x3', 1), ('skip_connect', 0), + ('skip_connect', 0), ('dil_conv_3x3', 2)], + normal_concat=[2, 3, 4, 5], + reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), + ('skip_connect', 2), ('max_pool_3x3', 1), + ('max_pool_3x3', 0), ('skip_connect', 2), + ('skip_connect', 2), ('max_pool_3x3', 1)], + reduce_concat=[2, 3, 4, 5]) + +cifar10_evolution = Genotype( + normal=[('max_pool_3x3', 0), ('max_pool_3x3', 1), + ('max_pool_3x3', 1), ('max_pool_3x3', 0), + ('dil_conv_5x5', 3), ('dil_conv_3x3', 2), + ('dil_conv_5x5', 4), ('dil_conv_5x5', 3)], + normal_concat=[2, 3, 4, 5], + reduce=[('max_pool_3x3', 0), ('max_pool_3x3', 1), + ('max_pool_3x3', 2), ('skip_connect', 0), + ('max_pool_3x3', 2), ('max_pool_3x3', 3), + ('dil_conv_5x5', 4), ('dil_conv_5x5', 3)], + reduce_concat=[2, 3, 4, 5]) + +cifar100_evolution = Genotype( + normal=[('max_pool_3x3', 1), ('max_pool_3x3', 0), + ('max_pool_3x3', 0), ('max_pool_3x3', 2), + ('max_pool_3x3', 0), ('max_pool_3x3', 2), + ('max_pool_3x3', 4), ('max_pool_3x3', 3)], + normal_concat=[2, 3, 4, 5], + reduce=[('dil_conv_5x5', 1), ('dil_conv_5x5', 0), + ('max_pool_3x3', 2), ('max_pool_3x3', 0), + ('max_pool_3x3', 2), ('max_pool_3x3', 0), + ('max_pool_3x3', 2), ('dil_conv_5x5', 4)], + reduce_concat=[2, 3, 4, 5]) + +mixture_evolution = Genotype( + normal=[('dil_conv_5x5', 1), ('dil_conv_5x5', 0), + ('max_pool_3x3', 2), ('dil_conv_5x5', 1), + ('max_pool_3x3', 2), ('max_pool_3x3', 3), + ('max_pool_3x3', 3), ('max_pool_3x3', 4)], + normal_concat=[2, 3, 4, 5], + reduce=[('dil_conv_5x5', 0), ('dil_conv_3x3', 1), + ('max_pool_3x3', 0), ('max_pool_3x3', 2), + ('max_pool_3x3', 2), ('dil_conv_5x5', 0), + ('max_pool_3x3', 2), ('dil_conv_5x5', 4)], + reduce_concat=[2, 3, 4, 5]) + +mdenas_pmnist = Genotype( + normal=[('max_pool_3x3', 0), ('avg_pool_3x3', 1), + ('max_pool_3x3', 1), ('sep_conv_3x3', 0), + ('avg_pool_3x3', 0), ('skip_connect', 3), + ('dil_conv_3x3', 1), ('dil_conv_3x3', 0)], + normal_concat=[2, 3, 4, 5], + reduce=[('dil_conv_3x3', 1), ('avg_pool_3x3', 0), + ('max_pool_3x3', 2), ('dil_conv_5x5', 1), + ('skip_connect', 2), ('avg_pool_3x3', 1), + ('avg_pool_3x3', 3), ('sep_conv_3x3', 4)], + reduce_concat=[2, 3, 4, 5] +) + +mdenas_mixture = Genotype( + normal=[('dil_conv_5x5', 0), ('dil_conv_5x5', 1), + ('avg_pool_3x3', 2), ('max_pool_3x3', 1), + ('sep_conv_3x3', 2), ('dil_conv_3x3', 1), + ('sep_conv_3x3', 1), ('avg_pool_3x3', 3)], + normal_concat=[2, 3, 4, 5], + reduce=[('dil_conv_3x3', 0), ('sep_conv_3x3', 1), + ('skip_connect', 2), ('avg_pool_3x3', 0), + ('dil_conv_5x5', 2), ('dil_conv_3x3', 0), + ('avg_pool_3x3', 0), ('skip_connect', 2)], + reduce_concat=[2, 3, 4, 5] +) diff --git a/rag_depth/src/automl/darts_model.py b/rag_depth/src/automl/darts_model.py new file mode 100644 index 0000000..914a3ed --- /dev/null +++ b/rag_depth/src/automl/darts_model.py @@ -0,0 +1,431 @@ +import numpy as np +import torch +import torch.nn as nn + +import utils + +from copy import deepcopy + +from automl.darts_operation import * + + + +class Cell(nn.Module): + + def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev): + super(Cell, self).__init__() + print(C_prev_prev, C_prev, C) + + if reduction_prev: + self.preprocess0 = FactorizedReduce(C_prev_prev, C) + else: + self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0) + self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0) + + if reduction: + op_names, indices = zip(*genotype.reduce) + concat = genotype.reduce_concat + else: + op_names, indices = zip(*genotype.normal) + concat = genotype.normal_concat + self._compile(C, op_names, indices, concat, reduction) + + def _compile(self, C, op_names, indices, concat, reduction): + assert len(op_names) == len(indices) + self._steps = len(op_names) // 2 + self._concat = concat + self.multiplier = len(concat) + + self._ops = nn.ModuleList() + for name, index in zip(op_names, indices): + stride = 2 if reduction and index < 2 else 1 + op = OPS[name](C, stride, True) + self._ops += [op] + self._indices = indices + + def forward(self, s0, s1): + s0 = self.preprocess0(s0) + s1 = self.preprocess1(s1) + + states = [s0, s1] + # todo: test + # print("num_cell: ", self._steps) + # print("len_indices", len(self._indices)) + for i in range(self._steps): + # print("idx: ", self._indices[2 * i]) + h1 = states[self._indices[2 * i]] + # print("idx: ", self._indices[2 * i + 1]) + h2 = states[self._indices[2 * i + 1]] + op1 = self._ops[2 * i] + op2 = self._ops[2 * i + 1] + h1 = op1(h1) + h2 = op2(h2) + s = h1 + h2 + states += [s] + return torch.cat([states[i] for i in self._concat], dim=1) + + +class Network(nn.Module): + + def __init__(self, input_size, task_classes, cell_nums, init_channel, genotype, device): + super(Network, self).__init__() + self.device = device + self.cell_nums = cell_nums + self.input_size = input_size + self.C = init_channel + self.task_classes = task_classes + self.genotype = [] # the genotypes which has been used in every model layer + self.new_genotype = [] # the new genotypes in every model layer in search stage + self.real_genotype = [] # the genotypes of each cell in every model layer + + self.stem_multiplier = 3 + self.length = {'stem': 1} + self.arch_init = {'stem': [0], 'fc': [0]} + self.mu_s = 2 # the number of mutate select + + C_curr = self.stem_multiplier * self.C + self.stem = nn.ModuleList([nn.Sequential( + nn.Conv2d(3, C_curr, 3, padding=1, bias=False), + nn.BatchNorm2d(C_curr) + )]) + + C_prev_prev, C_prev, C_curr = C_curr, C_curr, self.C + self.cells = nn.ModuleList([]) + reduction_prev = False + for i in range(self.cell_nums): + if i in [self.cell_nums // 3, 2 * self.cell_nums // 3]: + C_curr *= 2 + reduction = True + else: + reduction = False + cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev) + reduction_prev = reduction + self.cells.append(nn.ModuleList([cell])) + C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr + self.arch_init['cell' + str(i)] = [0] + self.length['cell'+str(i)] = 1 + # self.genotype[i] includes all kinds of genotypes that been used in the i cell layer + self.genotype.append([genotype]) + self.new_genotype.append([]) + self.real_genotype.append([genotype]) + + self.global_pooling = nn.AdaptiveAvgPool2d(1) + + self.classifier = nn.ModuleList([]) + for t, c in self.task_classes: + self.classifier.append(nn.Linear(C_prev, c)) + + # parameter for architecture search + self.a = torch.nn.ParameterDict({}) + # maps + self.map_length = [] + # The new models, model to train + self.new_models = None + self.model_to_train = None + + def forward(self, x, t, task_arch=None, path=None): + arch_stem = None + if task_arch is not None: + arch_stem = task_arch['stem'][0] + elif path is not None: + arch_stem = path[0] + s0 = s1 = self.stem[arch_stem](x) + + for i, cell in enumerate(self.cells): + arch_cell = None + if task_arch is not None: + arch_cell = task_arch['cell'+str(i)][0] + elif path is not None: + arch_cell = path[i+1] + s0, s1 = s1, cell[arch_cell](s0, s1) + + out = self.global_pooling(s1) + + logits = [] + for t, c in self.task_classes: + logits.append(self.classifier[t](out.view(out.size(0), -1))) + + return logits + + def expand(self, t, device='cuda'): + # expand the network to a super model + # 1 expand stem + # 1.1 reuse: reuse parameters and architecture + # 1.2 update: reuse architecture but update parameter + C_curr = self.stem_multiplier * self.C + self.stem.append(nn.Sequential(nn.Conv2d(3, C_curr, 3, padding=1, bias=False), + nn.BatchNorm2d(C_curr) + ).to(device)) + # 1.3 generate action parameter + num_l = self.length['stem'] + 1 + self.a['stem'] = nn.Parameter(torch.rand(num_l).to(device)) + self.map_length.append(num_l) + + # 2 expand cells + C_prev_prev, C_prev, C_curr = C_curr, C_curr, self.C + reduction_prev = False + for i in range(self.cell_nums): + if i in [self.cell_nums // 3, 2 * self.cell_nums // 3]: + C_curr *= 2 + reduction = True + else: + reduction = False + # 2.1 reuse: reuse parameters and architecture + # 2.2 update and search + multiplier = None + self.new_genotype[i] = [] # clear the new genotypes of layer i + # 2.2.1 update: reuse architecture but update parameter + for genotype in self.genotype[i]: + cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev).to(device) + multiplier = cell.multiplier + self.cells[i].append(cell) + # 2.2.2 search: search new architecture for cell + for genotype in self.genotype[i]: + new_genotypes = self.mutate_select(genotype) + for new_genotype in new_genotypes: + cell = Cell(new_genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev).to(device) + self.cells[i].append(cell) + self.new_genotype[i].append(new_genotype) + + reduction_prev = reduction + C_prev_prev, C_prev = C_prev, multiplier * C_curr + # 2.3 generate action parameter + # for each cell: reuse + update + mutate select + num_l = self.length['cell'+str(i)] + (1+self.mu_s) * len(self.genotype[i]) + self.a['cell'+str(i)] = nn.Parameter(torch.rand(num_l).to(device)) + self.map_length.append(num_l) + + # 3 get the new modules + self.get_new_model(t=t) + + def mutate_select(self, genotype): + num = self.mu_s + new_genotypes = [] + # (node2, [0,1]), (node3, [2,3]), (node4, [4,5]), (node5, [6,7]), + mutation_node = np.random.randint(0, 8, 2*num) + for i in range(num): + new_genotype = deepcopy(genotype) + prev_n = np.random.randint(0, mutation_node[i] // 2 + 2) + op_n = list(OPS.keys())[np.random.randint(4, 8)] + new_genotype.normal[mutation_node[i]] = (op_n, prev_n) + + prev_r = np.random.randint(0, mutation_node[i+num] // 2 + 2) + op_r = list(OPS.keys())[np.random.randint(4, 8)] + new_genotype.reduce[mutation_node[i+num]] = (op_r, prev_r) + + new_genotypes.append(new_genotype) + + return new_genotypes + + def mutate_evolve(self): + pass + + def get_new_model(self, t): + # get new model (update and search) + new_models = {'stem': [], 'fc': []} + # 1 stem + c = self.length['stem'] + new_models['stem'].append(c) + # 2 cells + for i in range(self.cell_nums): + c_1 = self.length['cell' + str(i)] + c_2 = len(self.genotype[i]) + num_l = c_1 + c_2 * (1 + self.mu_s) + new_models['cell' + str(i)] = [] + for k in range(c_1, num_l): + new_models['cell' + str(i)].append(k) + # # 3 pool + # new_models['pool'].append(t) + # 4 classifier + new_models['fc'].append(t) + self.new_models = new_models + + def get_archi_param(self): + return self.a + + def get_param(self, models): + params = [] + if 'stem' in models.keys(): + for idx in models['stem']: + params.append({'params': self.stem[idx].parameters()}) + + for i in range(self.cell_nums): + if 'cell' + str(i) in models.keys(): + for idx in models['cell' + str(i)]: + params.append({'params': self.cells[i][idx].parameters()}) + + # if 'pool' in models.keys(): + # for idx in models['pool']: + # params.append({'params': self.global_pooling[idx].parameters()}) + + if 'fc' in models.keys(): + for idx in models['fc']: + params.append({'params': self.classifier[idx].parameters()}) + + return params + + def modify_param(self, models, requires_grad=True): + if 'stem' in models.keys(): + for idx in models['stem']: + # print("Set stem {} as {}".format(idx, requires_grad)) + utils.modify_model(self.stem[idx], requires_grad) + + for i in range(self.cell_nums): + if 'cell' + str(i) in models.keys(): + for idx in models['cell' + str(i)]: + utils.modify_model(self.cells[i][idx], requires_grad) + # if 'pool' in models.keys(): + # for idx in models['pool']: + # utils.modify_model(self.global_pooling[idx], requires_grad) + + if 'fc' in models.keys(): + for idx in models['fc']: + # print("Set fc {} as {}".format(idx, requires_grad)) + utils.modify_model(self.classifier[idx], requires_grad) + + def modify_archi_param(self, requires_grad=True): + params = self.get_archi_param() + if requires_grad: + utils.unfreeze_parameter(params) + else: + utils.freeze_parameter(params) + + def unfreeze_path(self, path, t): + if path[0] >= self.length['stem']: + utils.unfreeze_model(self.stem[path[0]]) + for i in range(len(self.cells)): + if path[i+1] >= self.length['cell' + str(i)]: + utils.unfreeze_model(self.cells[i][path[i+1]]) + utils.unfreeze_model(self.classifier[t]) + + def regular_loss(self): + loss = 0.0 + # loss of stem + c = self.length['stem'] + g_stem = torch.exp(self.a['stem']) / torch.sum(torch.exp(self.a['stem'])) + loss += g_stem[c] * utils.model_size(self.stem[c]) + # loss of cells layer + for i in range(self.cell_nums): + # loss of cells layer i + c_1 = self.length['cell' + str(i)] + c_2 = len(self.genotype[i]) + g_cell = torch.exp(self.a['cell'+str(i)]) / torch.sum(torch.exp(self.a['cell'+str(i)])) + num_l = c_1 + (1 + self.mu_s) * c_2 + for k in range(c_1, num_l): + loss += g_cell[k] * utils.model_size(self.cells[i][k]) + + return loss + + def search_forward(self, x, t): + # 1 stem + g_stem = torch.exp(self.a['stem']) / torch.sum(torch.exp(self.a['stem'])) + # 1.1 stem: update + out_0 = out_1 = g_stem[-1] * self.stem[-1](x) + # 1.2 stem: reuse + c = self.length['stem'] + for i in range(c): + out_0 += g_stem[i] * self.stem[i](x) + out_1 += g_stem[i] * self.stem[i](x) + + # 2 cells layer + for i, cell in enumerate(self.cells): + g_cell = torch.exp(self.a['cell'+str(i)]) / torch.sum(torch.exp(self.a['cell'+str(i)])) + c_1 = self.length['cell' + str(i)] + c_2 = len(self.genotype[i]) + num_l = c_1 + (1 + self.mu_s) * c_2 + + x_0, x_1 = out_0, out_1 + out_0 = x_1 + + out_1 = g_cell[0] * cell[0](x_0, x_1) + for k in range(1, num_l): + out_1 += g_cell[k] * cell[k](x_0, x_1) + + out = self.global_pooling(out_1) + logits = [] + for t, c in self.task_classes: + logits.append(self.classifier[t](out.view(out.size(0), -1))) + + return logits + + def select(self, t, path=None): + # 1 define the container of new models to train and the best submodel + model_to_train = {'stem': [], 'fc': []} + best_archi = {'stem': [], 'fc': []} + best_op = {} # reuse: 0, update: 1, mutate 2 + # 2 stem + # 2.1 select the best architecture for stem + if path is None: + v, arg_v = torch.max(self.a['stem'].data, dim=0) + idx = deepcopy(arg_v.item()) + else: + idx = deepcopy(path[0]) + c = self.length['stem'] + if idx < c: # reuse + best_archi['stem'].append(idx) + best_op['stem'] = 0 + elif idx == c: # update + best_archi['stem'].append(c) + model_to_train['stem'].append(c) + best_op['stem'] = 1 + # 2.2 delete for stem + if idx != c: + del self.stem[c] + # 2.3 update the length + self.length['stem'] = len(self.stem) + + # 3 cells layer + for i in range(self.cell_nums): + # todo: test + # print("Select the cell: " + str(i)) + name = 'cell' + str(i) + g_name = 'genotype' + str(i) + model_to_train[name] = [] + best_archi[name] = [] + if path is None: + v, arg_v = torch.max(self.a[name].data, dim=0) + idx = deepcopy(arg_v.item()) + else: + idx = deepcopy(path[i+1]) + c_1 = self.length[name] + c_2 = len(self.genotype[i]) + num_l = c_1 + (1 + self.mu_s) * c_2 + # 3.1 select the best architecture for cell + if idx < c_1: # reuse the genotype of the idx cell in layer i + best_archi[name].append(idx) + best_op[name] = 0 + best_op[g_name] = deepcopy(self.real_genotype[i][idx]) + self.real_genotype[i].append(best_op[g_name]) + elif idx < c_1 + c_2: # update the idx-c_1 genotype in the used genotype types set + best_archi[name].append(c_1) + model_to_train[name].append(c_1) + best_op[name] = 1 + best_op[g_name] = deepcopy(self.genotype[i][idx-c_1]) + self.real_genotype[i].append(best_op[g_name]) + elif idx < num_l: # mutate + best_archi[name].append(c_1) + model_to_train[name].append(c_1) + self.genotype[i].append(deepcopy(self.new_genotype[i][idx-c_1-c_2])) + best_op[name] = 2 + best_op[g_name] = deepcopy(self.new_genotype[i][idx-c_1-c_2]) + self.real_genotype[i].append(best_op[g_name]) + # 3.2 delete for cell + cell_s = deepcopy(self.cells[i][idx]) + for k in range(c_1, num_l): + del self.cells[i][-1] + if idx >= c_1: + self.cells[i].append(cell_s) + + # 3.3 update the length + self.length[name] = len(self.cells[i]) + + # 4 the classifier and pool layer + model_to_train['fc'].append(t) + best_archi['fc'].append(t) + # model_to_train['pool'].append(t) + # best_archi['pool'].append(t) + + # 5 update the model to train + self.model_to_train = model_to_train + + return best_archi, best_op diff --git a/rag_depth/src/automl/darts_operation.py b/rag_depth/src/automl/darts_operation.py new file mode 100644 index 0000000..8da783c --- /dev/null +++ b/rag_depth/src/automl/darts_operation.py @@ -0,0 +1,107 @@ +import torch +import torch.nn as nn + +OPS = { + 'none': lambda C, stride, affine: Zero(stride), + 'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False), + 'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1), + 'skip_connect': lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine), + 'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine), + 'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine), + 'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine), + 'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine), + 'conv_7x1_1x7': lambda C, stride, affine: nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False), + nn.Conv2d(C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False), + nn.BatchNorm2d(C, affine=affine) + ), +} + + +class ReLUConvBN(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(ReLUConvBN, self).__init__() + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(c_in, c_out, kernel_size, stride=stride, padding=padding, bias=False), + nn.BatchNorm2d(c_out, affine=affine) + ) + + def forward(self, x): + return self.op(x) + + +class DilConv(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, dilation, affine=True): + super(DilConv, self).__init__() + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, + groups=c_in, bias=False), + nn.Conv2d(c_in, c_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_out, affine=affine), + ) + + def forward(self, x): + return self.op(x) + + +class SepConv(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(SepConv, self).__init__() + self.op = nn.Sequential( + nn.ReLU(inplace=False), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=c_in, bias=False), + nn.Conv2d(c_in, c_in, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_in, affine=affine), + nn.ReLU(inplace=False), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=1, padding=padding, groups=c_in, bias=False), + nn.Conv2d(c_in, c_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_out, affine=affine), + ) + + def forward(self, x): + return self.op(x) + + +class Identity(nn.Module): + + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return x + + +class Zero(nn.Module): + + def __init__(self, stride): + super(Zero, self).__init__() + self.stride = stride + + def forward(self, x): + if self.stride == 1: + return x.mul(0.) + return x[:, :, ::self.stride, ::self.stride].mul(0.) + + +class FactorizedReduce(nn.Module): + + def __init__(self, c_in, c_out, affine=True): + super(FactorizedReduce, self).__init__() + assert c_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv2d(c_in, c_out // 2, 1, stride=2, padding=0, bias=False) + self.conv_2 = nn.Conv2d(c_in, c_out // 2, 1, stride=2, padding=0, bias=False) + self.bn = nn.BatchNorm2d(c_out, affine=affine) + + def forward(self, x): + x = self.relu(x) + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1) + # out = self.bn(out) + return out + diff --git a/rag_depth/src/automl/darts_operation_plus.py b/rag_depth/src/automl/darts_operation_plus.py new file mode 100644 index 0000000..50a8bc8 --- /dev/null +++ b/rag_depth/src/automl/darts_operation_plus.py @@ -0,0 +1,107 @@ +import torch +import torch.nn as nn + +OPS = { + 'none': lambda C, stride, affine: Zero(stride), + 'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False), + 'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1), + 'skip_connect': lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine), + 'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine), + 'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine), + 'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine), + 'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine), + 'conv_7x1_1x7': lambda C, stride, affine: nn.Sequential( + nn.CELU(0.075), + nn.Conv2d(C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False), + nn.Conv2d(C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False), + nn.BatchNorm2d(C, affine=affine) + ), +} + + +class ReLUConvBN(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(ReLUConvBN, self).__init__() + self.op = nn.Sequential( + nn.CELU(0.075), + nn.Conv2d(c_in, c_out, kernel_size, stride=stride, padding=padding, bias=False), + nn.BatchNorm2d(c_out, affine=affine) + ) + + def forward(self, x): + return self.op(x) + + +class DilConv(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, dilation, affine=True): + super(DilConv, self).__init__() + self.op = nn.Sequential( + nn.CELU(0.075), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, + groups=c_in, bias=False), + nn.Conv2d(c_in, c_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_out, affine=affine), + ) + + def forward(self, x): + return self.op(x) + + +class SepConv(nn.Module): + + def __init__(self, c_in, c_out, kernel_size, stride, padding, affine=True): + super(SepConv, self).__init__() + self.op = nn.Sequential( + nn.CELU(0.075), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=c_in, bias=False), + nn.Conv2d(c_in, c_in, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_in, affine=affine), + nn.CELU(0.075), + nn.Conv2d(c_in, c_in, kernel_size=kernel_size, stride=1, padding=padding, groups=c_in, bias=False), + nn.Conv2d(c_in, c_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(c_out, affine=affine), + ) + + def forward(self, x): + return self.op(x) + + +class Identity(nn.Module): + + def __init__(self): + super(Identity, self).__init__() + + def forward(self, x): + return x + + +class Zero(nn.Module): + + def __init__(self, stride): + super(Zero, self).__init__() + self.stride = stride + + def forward(self, x): + if self.stride == 1: + return x.mul(0.) + return x[:, :, ::self.stride, ::self.stride].mul(0.) + + +class FactorizedReduce(nn.Module): + + def __init__(self, c_in, c_out, affine=True): + super(FactorizedReduce, self).__init__() + assert c_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv2d(c_in, c_out // 2, 1, stride=2, padding=0, bias=False) + self.conv_2 = nn.Conv2d(c_in, c_out // 2, 1, stride=2, padding=0, bias=False) + self.bn = nn.BatchNorm2d(c_out, affine=affine) + + def forward(self, x): + x = self.relu(x) + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1) + out = self.bn(out) + return out + diff --git a/rag_depth/src/automl/darts_search.py b/rag_depth/src/automl/darts_search.py new file mode 100644 index 0000000..74a563c --- /dev/null +++ b/rag_depth/src/automl/darts_search.py @@ -0,0 +1,211 @@ +import os +import sys +import time +import glob +import numpy as np +import torch +import logging +import argparse +import torch.nn as nn +import torch.utils +import torch.nn.functional as F +import torchvision.datasets as dset +import torch.backends.cudnn as cudnn + +import automl.darts_utils_cnn as utils + +from torch.autograd import Variable +from automl.darts_basicmodel import BasicNetwork +from automl.darts_model import Network +from automl.darts_architecture import Architect + + +class AutoSearch(object): + # Implements a NAS methods Darts + def __init__(self, num_cells, num_class=10, input_size=None, lr=0.025, lr_a=3e-4, lr_min=0.001, momentum=0.9, + weight_decay=3e-4, weight_decay_a=1e-3, grad_clip=5, unrolled=False, + device='cuda: 0', writer=None, exp_name=None, save_name='EXP', args=None): + self.num_cells = num_cells + self.num_classes = num_class + self.input_size = input_size + + self.device = device + self.writer = writer + self.exp_name = exp_name + + self.lr = lr + self.lr_a = lr_a + self.lr_min = lr_min + self.momentum = momentum + self.weight_decay = weight_decay + self.weight_decay_a = weight_decay_a + if args.mode == 'search': + self.weight_decay = args.lamb_w0 + self.weight_decay_a = args.lamb_wa0 + self.lr = args.lr0 + self.lr_a = args.lr_s0 + + self.grad_clip = grad_clip + self.unrolled = unrolled + self.save_name = save_name + + self.criterion = nn.CrossEntropyLoss().to(device) + self.model = BasicNetwork(self.input_size[0], 16, self.num_classes, self.num_cells, self.criterion, + device=self.device).to(device) + logging.info("param size = %fMB", utils.count_parameters_in_MB(self.model)) + + params = [] + for name, p in self.model.named_parameters(): + if name not in ['alphas_normal', 'alphas_reduce']: + params.append(p) + + self.optimizer = torch.optim.SGD(params=params, lr=self.lr, momentum=self.momentum, + weight_decay=self.weight_decay) + + self.architect = Architect(self.model, lr_a=self.lr_a, momentum=self.momentum, + weight_decay=self.weight_decay, weight_decay_a=self.weight_decay_a) + + def search(self, train_data, valid_data, batch_size, nepochs): + """ search a model genotype for the given task + + :param train_data: the dataset of training data of the given task + :param valid_data: the dataset of valid data of the given task + :param batch_size: the batch size of training + :param nepochs: the number of training epochs + :return: + genotype: the selected architecture for the given task + """ + # dataloader of training data + num_train = len(train_data) + indices = list(range(num_train)) + split = int(np.floor(0.5 * num_train)) + + train_queue = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), + pin_memory=True, num_workers=4) + # dataloader of valid date + valid_queue = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]), + pin_memory=True, num_workers=4) + # the scheduler of learning rate of model parameters optimizer + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, nepochs, + eta_min=self.lr_min) + best_loss = np.inf + best_model = utils.get_model(self.model) + + for epoch in range(nepochs): + lr = scheduler.get_lr()[0] + logging.info('epoch %d lr %e', epoch, lr) + print('epoch: {} lr: {}'.format(epoch, lr)) + + genotype = self.model.genotype() + logging.info('genotype = %s', genotype) + + arch_n = F.softmax(self.model.arch_parameters()["alphas_normal"], dim=-1) + arch_r = F.softmax(self.model.arch_parameters()["alphas_reduce"], dim=-1) + print(arch_n) + # self.writer.add_images('CellArchValue/Normal', + # arch_n, global_step=epoch, dataformats='HW') + self.writer.add_histogram('CellArchHist/Normal', + arch_n, global_step=epoch) + print(arch_r) + # self.writer.add_images('CellArchValue/Reduce', + # arch_r, global_step=epoch, dataformats='HW') + self.writer.add_histogram('CellArchHist/Reduce', + arch_r, global_step=epoch) + + # training + train_acc, train_obj = self.train(train_queue, valid_queue, lr) + print('train_acc: {}'.format(train_acc)) + + # validation + valid_acc, valid_obj = self.eval(valid_queue) + print('valid_acc: {}'.format(valid_acc)) + + # logging + self.writer.add_scalars('Search_Loss/Task: 0', + {'train_loss': train_obj, 'valid_loss': valid_obj}, + global_step=epoch) + self.writer.add_scalars('Search_Accuracy/Task: 0', + {'train_acc': train_acc, 'valid_acc': valid_acc}, + global_step=epoch) + + # adjust learning according the scheduler + scheduler.step() + + if valid_obj < best_loss: + best_model = utils.get_model(self.model) + best_loss = valid_obj + + # the best model and its architecture + utils.set_model(self.model, best_model) + print("The best architecture is", self.model.genotype()) + return self.model.genotype() + + def train(self, train_queue, valid_queue, lr): + objs = utils.AverageMeter() + top1 = utils.AverageMeter() + # top5 = utils.AverageMeter() + + for step, (x, y) in enumerate(train_queue): + self.model.train() + n = x.size(0) + + x, y = x.to(self.device), y.to(self.device) + + # get a random mini batch from the search queue with replacement + x_s, y_s = next(iter(valid_queue)) + x_s, y_s = x_s.to(self.device), y_s.to(self.device) + + self.architect.step(x, y, x_s, y_s, lr, self.optimizer, unrolled=self.unrolled) + + self.optimizer.zero_grad() + logits = self.model(x) + loss = self.criterion(logits, y) + + loss.backward() + nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip) + self.optimizer.step() + + prec1 = utils.accuracy(logits, y, topk=1) + objs.update(loss.item(), n) + top1.update(prec1.item(), n) + # top5.update(prec5.item(), n) + + if step % 100 == 0: + logging.info('train %03d %e %f %f', step, objs.avg, top1.avg) + print('train: {} loss: {} acc: {}'.format(step, objs.avg, top1.avg)) + + return top1.avg, objs.avg + + def eval(self, valid_queue): + objs = utils.AverageMeter() + top1 = utils.AverageMeter() + # top5 = utils.AverageMeter() + self.model.eval() + + for step, (x, y) in enumerate(valid_queue): + + x, y = x.to(self.device), y.to(self.device) + + logits = self.model(x) + loss = self.criterion(logits, y) + + prec1 = utils.accuracy(logits, y, topk=1) + n = x.size(0) + objs.update(loss.item(), n) + top1.update(prec1.item(), n) + # top5.update(prec5.item(), n) + + if step % 100 == 0: + logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg) + print('valid: {} loss: {} acc: {}'.format(step, objs.avg, top1.avg)) + + return top1.avg, objs.avg + + def create_model(self, archi, num_cells, input_size, task_classes, init_channel): + + return Network(input_size, task_classes, num_cells, init_channel, archi, device=self.device) + diff --git a/rag_depth/src/automl/darts_utils_cnn.py b/rag_depth/src/automl/darts_utils_cnn.py new file mode 100644 index 0000000..4af21b1 --- /dev/null +++ b/rag_depth/src/automl/darts_utils_cnn.py @@ -0,0 +1,59 @@ +import os +import numpy as np +import torch +import shutil +import torchvision.transforms as transforms + +from copy import deepcopy +from torch.autograd import Variable + + + +def count_parameters_in_MB(model): + return np.sum(np.prod(v.size()) for name, v in model.named_parameters() if "auxiliary" not in name)/1e6 + + +def save_checkpoint(state, is_best, save_path): + filename = os.path.join(save_path, 'checkpoint.pth.tar') + torch.save(state, filename) + if is_best: + best_filename = os.path.join(save_path, 'model_best.pth.tar') + shutil.copyfile(filename, best_filename) + + +def get_model(model): + return deepcopy(model.state_dict()) + + +def set_model(model, state_dict): + model.load_state_dict(deepcopy(state_dict)) + + +def save(model, model_path): + torch.save(model.state_dict(), model_path) + + +def load(model, model_path): + model.load_state_dict(torch.load(model_path)) + + +def drop_path(x, drop_prob): + if drop_prob > 0.: + keep_prob = 1.-drop_prob + mask = Variable(torch.cuda.FloatTensor(x.size(0), 1, 1, 1).bernoulli_(keep_prob)) + x.div_(keep_prob) + x.mul_(mask) + return x + + +def create_exp_dir(path, scripts_to_save=None): + if not os.path.exists(path): + os.mkdir(path) + print('Experiment dir : {}'.format(path)) + + if scripts_to_save is not None: + os.mkdir(os.path.join(path, 'scripts')) + for script in scripts_to_save: + dst_file = os.path.join(path, 'scripts', os.path.basename(script)) + shutil.copyfile(script, dst_file) + diff --git a/rag_depth/src/automl/genotypes_2d.py b/rag_depth/src/automl/genotypes_2d.py new file mode 100644 index 0000000..bea5472 --- /dev/null +++ b/rag_depth/src/automl/genotypes_2d.py @@ -0,0 +1,10 @@ +from collections import namedtuple + +#Genotype = namedtuple('Genotype_2D', 'cell cell_concat') +Genotype = namedtuple('Genotype_2D', 'normal normal_concat reduce reduce_concat') + +PRIMITIVES = [ + 'skip_connect_2d', + 'conv_3x3'] + + diff --git a/rag_depth/src/automl/genotypes_3d.py b/rag_depth/src/automl/genotypes_3d.py new file mode 100644 index 0000000..d4cc6a1 --- /dev/null +++ b/rag_depth/src/automl/genotypes_3d.py @@ -0,0 +1,10 @@ +from collections import namedtuple + +#Genotype = namedtuple('Genotype', 'cell cell_concat') +Genotype_3D = namedtuple('Genotype', 'reduce reduce_concat') + +PRIMITIVES_3D = [ + 'skip_connect_3d', + '3d_conv_3x3' +] + diff --git a/rag_depth/src/automl/mdenas_basicmodel.py b/rag_depth/src/automl/mdenas_basicmodel.py new file mode 100644 index 0000000..350de3c --- /dev/null +++ b/rag_depth/src/automl/mdenas_basicmodel.py @@ -0,0 +1,160 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from copy import deepcopy +import numpy as np + + +from automl.genotypes_2d import PRIMITIVES +from automl.genotypes_2d import Genotype +from automl.genotypes_3d import PRIMITIVES_3D +from automl.genotypes_3d import Genotype_3D + +from automl.build_model_2d import AutoFeature +from automl.build_model_3d import AutoMatching + + +class DisparityRegression(nn.Module): + def __init__(self, maxdisp): + super(DisparityRegression, self).__init__() + self.maxdisp = maxdisp + + def forward(self, x): + assert(x.is_contiguous() == True) + with torch.cuda.device_of(x): + disp = torch.reshape(torch.arange(0, self.maxdisp, device=torch.cuda.current_device(), dtype=torch.float32),[1,self.maxdisp,1,1]) + disp = disp.repeat(x.size()[0], 1, x.size()[2], x.size()[3]) + out = torch.sum(x * disp, 1) + return out + + +class Disp(nn.Module): + def __init__(self, maxdisp=192): + super(Disp, self).__init__() + self.maxdisp = maxdisp + self.softmax = nn.Softmin(dim=1) + self.disparity = DisparityRegression(maxdisp=self.maxdisp) + + def forward(self, x): + x = F.interpolate(x, [self.maxdisp, x.size()[3]*3, x.size()[4]*3], mode='trilinear', align_corners=False) + x = torch.squeeze(x, 1) + x = self.softmax(x) + x = self.disparity(x) + return x + + +def upsample(x, scale_factor=2, mode="bilinear", align_corners=False): + """Upsample input tensor by a factor of 2 + """ + return F.interpolate(x, scale_factor=scale_factor, mode=mode, align_corners=align_corners) + +class DispHead(nn.Module): + def __init__(self, input_dim=100): + super(DispHead, self).__init__() + # self.norm1 = nn.BatchNorm2d(input_dim) + self.conv1 = nn.Conv2d(input_dim, 1, 3, padding=1) + # self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + + def forward(self, x, scale): + # x = self.relu(self.norm1(x)) + x = self.sigmoid(self.conv1(x)) + if scale > 1: + x = upsample(x, scale_factor=scale) + return x + + + +class BasicNetwork(nn.Module): + + #def __init__(self, input_c, C, num_classes, layers, criterion, steps=3, multiplier=4, stem_multiplier=3, device='cuda:0'): + def __init__(self, steps=3, multiplier=4, stem_multiplier=3, device='cuda:0'): + super(BasicNetwork, self).__init__() + self._steps = steps # the number of intermediate nodes + self._multiplier = multiplier + self.device = device + self.num_ops = len(PRIMITIVES) + self.num_edges = sum(1 for i in range(self._steps) for n in range(2 + i)) + + + self.feature = AutoFeature() + self.matching = AutoMatching() + + self.maxdisp = 192 + self.disp = Disp(self.maxdisp) + self.depth_head = DispHead(input_dim=1) + self.max_depth = 80 + + # initialize the probabilities + self.p = None + self._initialize_p() + + def new(self): + model_new = BasicNetwork().to(self.device) + model_new.p = deepcopy(self.probability()) + + return model_new + + def forward(self, left, right, fea_ops, mat_ops): + # stereo matching pipeline + x = self.feature(left, fea_ops) + #y = self.feature(right, fea_ops) + + ''' + with torch.cuda.device_of(x): + cost = x.new().resize_(x.size()[0], x.size()[1]*2, int(self.maxdisp/3), x.size()[2], x.size()[3]).zero_() + for i in range(int(self.maxdisp/3)): + if i > 0 : + cost[:,:x.size()[1], i,:,i:] = x[:,:,:,i:] + cost[:,x.size()[1]:, i,:,i:] = y[:,:,:,:-i] + else: + cost[:,:x.size()[1],i,:,i:] = x + cost[:,x.size()[1]:,i,:,i:] = y + ''' + + cost = x + cost = self.matching(cost, mat_ops) # [B, 1, 64, 128] 1/3 original scale + #disp = self.disp(cost) + depth = self.depth_head(cost, 3) + + disp = torch.squeeze(depth, 1) * self.max_depth + + return disp + + def _initialize_p(self): + k = self.num_edges + num_ops = self.num_ops + self.p = { + "normal": torch.full((k, num_ops), 1/num_ops), + "reduce": torch.full((k, num_ops), 1/num_ops) + } + + def probability(self): + return self.p + + def genotype(self): + + def _parse(alphas, steps): + gene = [] + start = 0 + n = 2 + for i in range(steps): + end = start + n + edges = sorted(range(start, end), key=lambda x: -np.max(alphas[x, 1:])) # ignore none value + top2edges = edges[:2] + for j in top2edges: + best_op_index = np.argmax(alphas[j]) # this can include none op + gene.append([j, best_op_index]) + start = end + n += 1 + return np.array(gene) + + normalized_fea = F.softmax(self.p['normal'], dim=-1).numpy() + normalized_mat = F.softmax(self.p['reduce'], dim=-1).numpy() + gene_normal = _parse(normalized_fea, self._steps) + gene_reduce = _parse(normalized_mat, self._steps) + genotype = Genotype(normal=gene_normal, normal_concat=None, reduce=gene_reduce, reduce_concat=None) + + return genotype + diff --git a/rag_depth/src/automl/mdenas_search.py b/rag_depth/src/automl/mdenas_search.py new file mode 100644 index 0000000..32c92d6 --- /dev/null +++ b/rag_depth/src/automl/mdenas_search.py @@ -0,0 +1,299 @@ +import numpy as np +import torch +import logging +import torch.nn as nn +import torch.nn.functional as F +import torch.utils + +import automl.darts_utils_cnn as utils + +from utilstool.metrics import D1_metric, Thres_metric, EPE_metric +from utilstool.experiment import * + +from automl.mdenas_basicmodel import BasicNetwork + +def compute_errors(gt, pred): + thresh = np.maximum((gt / pred), (pred / gt)) + d1 = (thresh < 1.25).mean() + d2 = (thresh < 1.25 ** 2).mean() + d3 = (thresh < 1.25 ** 3).mean() + + rms = (gt - pred) ** 2 + rms = np.sqrt(rms.mean()) + + log_rms = (np.log(gt) - np.log(pred)) ** 2 + log_rms = np.sqrt(log_rms.mean()) + + abs_rel = np.mean(np.abs(gt - pred) / gt) + sq_rel = np.mean(((gt - pred) ** 2) / gt) + + err = np.log(pred) - np.log(gt) + silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100 + + err = np.abs(np.log10(pred) - np.log10(gt)) + log10 = np.mean(err) + + return [silog, abs_rel, log10, rms, sq_rel, log_rms, d1, d2, d3] + + +class AutoSearch(object): + # Implements a NAS methods MdeNAS + def __init__(self, lr_min=0.001, momentum=0.9, weight_decay=3e-4, grad_clip=5, device='cuda: 0', writer=None, exp_name=None, save_name='EXP', args=None): + + self.device = device + self.writer = writer + self.exp_name = exp_name + + #import pdb; pdb.set_trace() + self.lr_min = lr_min + self.momentum = momentum + self.weight_decay = weight_decay + + #import pdb; pdb.set_trace() + + if args.mode == 'search': + self.lr = args.c_lr + self.lr_a = args.c_lr_a + self.weight_decay = args.c_lamb + self.c_epochs = args.c_epochs + self.c_batch = args.c_batch + + self.grad_clip = grad_clip + self.save_name = save_name + + self.max_disp = 192 + + self.model = BasicNetwork().to(device) + logging.info("param size = %fMB", utils.count_parameters_in_MB(self.model)) + + self.optimizer = torch.optim.SGD(params=self.model.parameters(), lr=self.lr, momentum=self.momentum, + weight_decay=self.weight_decay) + + self.silog_criterion = silog_loss() + + def search(self, t, train_data, valid_data, batch_size, nepochs): + """ search a model genotype for the given task + + :param train_data: the dataset of training data of the given task + :param valid_data: the dataset of valid data of the given task + :param batch_size: the batch size of training + :param nepochs: the number of training epochs + :return: + genotype: the selected architecture for the given task + """ + # dataloader of training data + num_train = len(train_data) + indices = list(range(num_train)) + split = int(np.floor(0.5 * num_train)) + + train_queue = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), + pin_memory=True, num_workers=4) + # dataloader of valid date + valid_queue = torch.utils.data.DataLoader( + train_data, batch_size=batch_size, + sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]), + pin_memory=True, num_workers=4) + # the scheduler of learning rate of model parameters optimizer + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, nepochs, + eta_min=self.lr_min) + best_loss = np.inf + best_model = utils.get_model(self.model) + + h_e = { + 'normal': torch.full((self.model.num_edges, self.model.num_ops), 0, dtype=torch.long), + 'reduce': torch.full((self.model.num_edges, self.model.num_ops), 0, dtype=torch.long) + } + h_a = { + 'normal': torch.full((self.model.num_edges, self.model.num_ops), 0.0), + 'reduce': torch.full((self.model.num_edges, self.model.num_ops), 0.0) + } + for epoch in range(nepochs): + # 0 prepare + lr = scheduler.get_lr()[0] + logging.info('epoch %d lr %e', epoch, lr) + print('epoch: {} lr: {}'.format(epoch, lr)) + genotype = self.model.genotype() + logging.info('genotype = %s', genotype) + # 1 sample + p_n = self.model.probability()["normal"] + p_r = self.model.probability()["reduce"] + self.writer.add_histogram('CellArchHist/Normal', + p_n, global_step=epoch) + self.writer.add_histogram('CellArchHist/Reduce', + p_r, global_step=epoch) + selected_ops = { + 'normal': torch.multinomial(p_n, 1).view(-1), + 'reduce': torch.multinomial(p_r, 1).view(-1) + } + # 2 train + fea_ops = selected_ops['normal'] + mat_ops = selected_ops['reduce'] + train_d1, train_obj, train_epe = self.train(train_queue, batch_size, fea_ops, mat_ops) + #print('train_d1: {}'.format(train_d1)) + valid_d1, valid_obj, valid_epe = self.eval(valid_queue, batch_size, fea_ops, mat_ops) + #print('valid_d1: {}'.format(valid_d1)) + #valid_d1, valid_obj = train_d1, train_obj + # logging + self.writer.add_scalars('Search_Cell_Loss/Task: {}'.format(t), + {'train_loss': train_obj, 'valid_loss': valid_obj}, + global_step=epoch) + self.writer.add_scalars('Search_Cell_D1_All/Task: {}'.format(t), + {'train_d1': train_d1, 'valid_d1': valid_d1}, + global_step=epoch) + self.writer.add_scalars('Search_Cell_EPE/Task: {}'.format(t), + {'train_epe': train_epe, 'valid_epe': valid_epe}, + global_step=epoch) + + # 3 update h_e and h_a + for cell_type in ['normal', 'reduce']: + # for each edge + for i, idx in enumerate(selected_ops[cell_type]): + h_e[cell_type][i][idx] += 1 + h_a[cell_type][i][idx] = 1 - valid_d1 + + # 4 update the probability + for k in range(self.model.num_edges): + dh_e_k = { + 'normal': torch.reshape(h_e['normal'][k], (1, -1)) - torch.reshape(h_e['normal'][k], (-1, 1)), + 'reduce': torch.reshape(h_e['reduce'][k], (1, -1)) - torch.reshape(h_e['reduce'][k], (-1, 1)) + } + dh_a_k = { + 'normal': torch.reshape(h_a['normal'][k], (1, -1)) - torch.reshape(h_a['normal'][k], (-1, 1)), + 'reduce': torch.reshape(h_a['reduce'][k], (1, -1)) - torch.reshape(h_a['reduce'][k], (-1, 1)) + } + for cell_type in ['normal', 'reduce']: + # vector1 = torch.sum((dh_e_k[cell_type] < 0) * (dh_a_k[cell_type] > 0), dim=1) + vector1 = torch.sum((dh_e_k[cell_type] < 0) * (dh_a_k[cell_type] > 0), dim=0) + # vector2 = torch.sum((dh_e_k[cell_type] > 0) * (dh_a_k[cell_type] < 0), dim=1) + vector2 = torch.sum((dh_e_k[cell_type] > 0) * (dh_a_k[cell_type] < 0), dim=0) + self.model.p[cell_type][k] += (self.lr_a * (vector1-vector2).float()) + self.model.p[cell_type][k] = F.softmax(self.model.p[cell_type][k]) + + # adjust learning according the scheduler + scheduler.step() + + if valid_obj < best_loss: + best_model = utils.get_model(self.model) + best_loss = valid_obj + + # the best model and its architecture + utils.set_model(self.model, best_model) + print("The best architecture is", self.model.genotype()) + return self.model.genotype() + + def train(self, train_queue, batch_size, fea_ops, mat_ops): + avg_train_scalars = AverageMeterDict() + abs_rel_sum = 0 + rmse_sum = 0 + count = 0 + for step, sample in enumerate(train_queue): + self.model.train() + imgL, imgR, disp_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + self.optimizer.zero_grad() + disp_est = self.model(imgL, imgR, fea_ops, mat_ops) + #mask = (disp_gt < self.max_disp) & (disp_gt > 0) + #loss = F.smooth_l1_loss(disp_est[mask], disp_gt[mask], size_average=True) + mask = disp_gt > 0 + loss = self.silog_criterion.forward(disp_est, disp_gt, mask.to(torch.bool)) + + loss.backward() + nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip) + self.optimizer.step() + + scalar_outputs = {"loss": loss} + with torch.no_grad(): + epe = EPE_metric(disp_est, disp_gt, mask) + scalar_outputs["EPE"] = epe + d1 = D1_metric(disp_est, disp_gt, mask) + scalar_outputs["D1"] = d1 + scalar_outputs["Thres1"] = Thres_metric(disp_est, disp_gt, mask, 1.0) + scalar_outputs["Thres2"] = Thres_metric(disp_est, disp_gt, mask, 2.0) + scalar_outputs["Thres3"] = Thres_metric(disp_est, disp_gt, mask, 3.0) + scalar_outputs = tensor2float(scalar_outputs) + avg_train_scalars.update(scalar_outputs) + + disp_gt = disp_gt.cpu().numpy() + disp_est = disp_est.cpu().numpy() + mask = disp_gt > 0 + depth_errors = compute_errors(disp_gt[mask], disp_est[mask]) + abs_rel_sum += depth_errors[1] + rmse_sum += depth_errors[3] + count += 1 + + if step % 5 == 0: + logging.info('train %03d %e %f %f %f', step, loss.item(), tensor2float(epe), tensor2float(d1)) + #print('train: {} loss: {} EPE: {} D1: {}'.format(step, loss.item(), tensor2float(epe), tensor2float(d1))) + print('train: {} loss: {} abs_rel: {} rmse: {}'.format(step, loss.item(), depth_errors[1], depth_errors[3])) + #if step * batch_size > 200: + # break + #if step > 20: + # break + avg_train_scalars = avg_train_scalars.mean() + loss_avg = avg_train_scalars["loss"] + d1_avg = avg_train_scalars["D1"] + epe_avg = avg_train_scalars["EPE"] + + abs_rel_avg = abs_rel_sum / count + rmse_avg = rmse_sum / count + + #return d1_avg, loss_avg, epe_avg + return abs_rel_avg, loss_avg, rmse_avg + + def eval(self, valid_queue, batch_size, fea_ops, mat_ops): + avg_eval_scalars = AverageMeterDict() + abs_rel_sum = 0 + rmse_sum = 0 + count = 0 + self.model.eval() + with torch.no_grad(): + for step, sample in enumerate(valid_queue): + imgL, imgR, disp_gt = sample['left'].to(self.device), sample['right'].to(self.device), sample['disparity'].to(self.device) + disp_est = self.model(imgL, imgR, fea_ops, mat_ops) + #mask = (disp_gt < self.max_disp) & (disp_gt > 0) + #loss = F.smooth_l1_loss(disp_est[mask], disp_gt[mask], size_average=True) + mask = disp_gt > 0 + loss = self.silog_criterion.forward(disp_est, disp_gt, mask.to(torch.bool)) + scalar_outputs = {"loss": loss} + epe = EPE_metric(disp_est, disp_gt, mask) + scalar_outputs["EPE"] = epe + d1 = D1_metric(disp_est, disp_gt, mask) + scalar_outputs["D1"] = d1 + scalar_outputs["Thres1"] = Thres_metric(disp_est, disp_gt, mask, 1.0) + scalar_outputs["Thres2"] = Thres_metric(disp_est, disp_gt, mask, 2.0) + scalar_outputs["Thres3"] = Thres_metric(disp_est, disp_gt, mask, 3.0) + scalar_outputs = tensor2float(scalar_outputs) + avg_eval_scalars.update(scalar_outputs) + + disp_gt = disp_gt.cpu().numpy() + disp_est = disp_est.cpu().numpy() + mask = disp_gt > 0 + depth_errors = compute_errors(disp_gt[mask], disp_est[mask]) + abs_rel_sum += depth_errors[1] + rmse_sum += depth_errors[3] + count += 1 + + if step % 5 == 0: + logging.info('valid %03d %e %f %f %f', step, loss.item(), tensor2float(epe), tensor2float(d1)) + #print('valid: {} loss: {} EPE: {} D1: {}'.format(step, loss.item(), tensor2float(epe), tensor2float(d1))) + print('train: {} loss: {} abs_rel: {} rmse: {}'.format(step, loss.item(), depth_errors[1], depth_errors[3])) + #if step * batch_size > 200: + # break + #if step > 20: + # break + + avg_eval_scalars = avg_eval_scalars.mean() + loss_avg = avg_eval_scalars["loss"] + d1_avg = avg_eval_scalars["D1"] + epe_avg = avg_eval_scalars["EPE"] + + abs_rel_avg = abs_rel_sum / count + rmse_avg = rmse_sum / count + + #return d1_avg, loss_avg, epe_avg + return abs_rel_avg, loss_avg, rmse_avg + + + + diff --git a/rag_depth/src/automl/operations_2d.py b/rag_depth/src/automl/operations_2d.py new file mode 100644 index 0000000..81539cf --- /dev/null +++ b/rag_depth/src/automl/operations_2d.py @@ -0,0 +1,214 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +OPS_2d = { + 'skip_connect_2d': lambda C, stride: Identity_2d() if stride == 1 else FactorizedReduce(C, C), + 'conv_3x3': lambda C, stride: ConvBR_2d(C, C, 3, stride, 1) +} + +class NaiveBN_2d(nn.Module): + def __init__(self, C_out, momentum=0.1): + super(NaiveBN_2d, self).__init__() + self.op = nn.Sequential( + nn.BatchNorm2d(C_out), + nn.ReLU() + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class ConvBR_2d(nn.Module): + def __init__(self, C_in, C_out, kernel_size, stride, padding, bn=True, relu=True): + super(ConvBR_2d, self).__init__() + self.relu = relu + self.use_bn = bn + + self.conv = nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False) + self.bn = nn.BatchNorm2d(C_out) + self._initialize_weights() + + def forward(self, x): + x = self.conv(x) + if self.use_bn: + x = self.bn(x) + if self.relu: + x = F.relu(x, inplace=True) + return x + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class SepConv_2d(nn.Module): + def __init__(self, C_in, C_out, kernel_size, stride, padding): + super(SepConv_2d, self).__init__() + self.op = nn.Sequential( + nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, + bias=False), + nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(C_in), + nn.ReLU(inplace=False), + nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False), + nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm2d(C_out), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class Identity_2d(nn.Module): + def __init__(self): + super(Identity_2d, self).__init__() + self._initialize_weights() + + def forward(self, x): + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class FactorizedReduce_2d(nn.Module): + def __init__(self, C_in, C_out): + super(FactorizedReduce_2d, self).__init__() + assert C_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) + self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) + self.bn = nn.BatchNorm2d(C_out) + self._initialize_weights() + + def forward(self, x): + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1) + out = self.bn(out) + out = self.relu(out) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class DoubleFactorizedReduce_2d(nn.Module): + def __init__(self, C_in, C_out): + super(DoubleFactorizedReduce_2d, self).__init__() + assert C_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=4, padding=0, bias=False) + self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=4, padding=0, bias=False) + self.bn = nn.BatchNorm2d(C_out) + self._initialize_weights() + + def forward(self, x): + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1) + out = self.bn(out) + out = self.relu(out) + return out + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class FactorizedIncrease_2d(nn.Module): + def __init__(self, in_channel, out_channel): + super(FactorizedIncrease_2d, self).__init__() + + self._in_channel = in_channel + self.op = nn.Sequential( + nn.Upsample(scale_factor=2, mode="bilinear"), + nn.Conv2d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm2d(out_channel), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class DoubleFactorizedIncrease_2d(nn.Module): + def __init__(self, in_channel, out_channel): + super(DoubleFactorizedIncrease_2d, self).__init__() + + self._in_channel = in_channel + self.op = nn.Sequential( + nn.Upsample(scale_factor=2, mode="bilinear"), + nn.Conv2d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm2d(out_channel), + nn.ReLU(inplace=False), + nn.Upsample(scale_factor=2, mode="bilinear"), + nn.Conv2d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm2d(out_channel), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + diff --git a/rag_depth/src/automl/operations_3d.py b/rag_depth/src/automl/operations_3d.py new file mode 100644 index 0000000..cb5bf95 --- /dev/null +++ b/rag_depth/src/automl/operations_3d.py @@ -0,0 +1,215 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +OPS_3d = { + 'skip_connect_3d': lambda C, stride: Identity_3d() if stride == 1 else FactorizedReduce(C, C), + '3d_conv_3x3': lambda C, stride: ConvBR_3d(C, C, 3, stride, 1) +} + +class NaiveBN_3d(nn.Module): + def __init__(self, C_out, momentum=0.1): + super(NaiveBN, self).__init__() + self.op = nn.Sequential( + nn.BatchNorm3d(C_out), + nn.ReLU() + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class ConvBR_3d(nn.Module): + def __init__(self, C_in, C_out, kernel_size, stride, padding, bn=True, relu=True): + super(ConvBR_3d, self).__init__() + self.relu = relu + self.use_bn = bn + + self.conv = nn.Conv3d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False) + self.bn = nn.BatchNorm3d(C_out) + self._initialize_weights() + + def forward(self, x): + x = self.conv(x) + if self.use_bn: + x = self.bn(x) + if self.relu: + x = F.relu(x, inplace=True) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class SepConv_3d(nn.Module): + def __init__(self, C_in, C_out, kernel_size, stride, padding): + super(SepConv_3d, self).__init__() + self.op = nn.Sequential( + nn.Conv3d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, + bias=False), + nn.Conv3d(C_in, C_in, kernel_size=1, padding=0, bias=False), + nn.BatchNorm3d(C_in), + nn.ReLU(inplace=False), + nn.Conv3d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False), + nn.Conv3d(C_in, C_out, kernel_size=1, padding=0, bias=False), + nn.BatchNorm3d(C_out), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class Identity_3d(nn.Module): + def __init__(self): + super(Identity_3d, self).__init__() + self._initialize_weights() + + def forward(self, x): + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv3d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class FactorizedReduce_3d(nn.Module): + def __init__(self, C_in, C_out): + super(FactorizedReduce_3d, self).__init__() + assert C_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv3d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) + self.conv_2 = nn.Conv3d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) + self.bn = nn.BatchNorm3d(C_out) + self._initialize_weights() + + def forward(self, x): + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1) + out = self.bn(out) + out = self.relu(out) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv3d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class DoubleFactorizedReduce_3d(nn.Module): + def __init__(self, C_in, C_out): + super(DoubleFactorizedReduce_3d, self).__init__() + assert C_out % 2 == 0 + self.relu = nn.ReLU(inplace=False) + self.conv_1 = nn.Conv3d(C_in, C_out // 2, 1, stride=4, padding=0, bias=False) + self.conv_2 = nn.Conv3d(C_in, C_out // 2, 1, stride=4, padding=0, bias=False) + self.bn = nn.BatchNorm3d(C_out) + self._initialize_weights() + + def forward(self, x): + out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:, 1:])], dim=1) + out = self.bn(out) + out = self.relu(out) + return out + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +class FactorizedIncrease_3d(nn.Module): + def __init__(self, in_channel, out_channel): + super(FactorizedIncrease_3d, self).__init__() + + self._in_channel = in_channel + self.op = nn.Sequential( + nn.Upsample(scale_factor=2, mode="trilinear"), + nn.Conv3d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm3d(out_channel), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class DoubleFactorizedIncrease_3d(nn.Module): + def __init__(self, in_channel, out_channel): + super(DoubleFactorizedIncrease_3d, self).__init__() + + self._in_channel = in_channel + self.op = nn.Sequential( + nn.Upsample(scale_factor=2, mode="trilinear"), + nn.Conv3d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm3d(out_channel), + nn.ReLU(inplace=False), + nn.Upsample(scale_factor=2, mode="trilinear"), + nn.Conv3d(self._in_channel, out_channel, 1, stride=1, padding=0), + nn.BatchNorm3d(out_channel), + nn.ReLU(inplace=False) + ) + self._initialize_weights() + + def forward(self, x): + return self.op(x) + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm3d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + diff --git a/rag_depth/src/dataloaders/__init__.py b/rag_depth/src/dataloaders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rag_depth/src/dataloaders/data_io.py b/rag_depth/src/dataloaders/data_io.py new file mode 100644 index 0000000..e250c3d --- /dev/null +++ b/rag_depth/src/dataloaders/data_io.py @@ -0,0 +1,58 @@ +import numpy as np +import re +import torchvision.transforms as transforms + + +def get_transform(): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + + return transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=mean, std=std), + ]) + + +# read all lines in a file +def read_all_lines(filename): + with open(filename) as f: + lines = [line.rstrip() for line in f.readlines()] + return lines + + +# read an .pfm file into numpy array, used to load SceneFlow disparity files +def pfm_imread(filename): + file = open(filename, 'rb') + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().decode('utf-8').rstrip() + if header == 'PF': + color = True + elif header == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode('utf-8')) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data, scale diff --git a/rag_depth/src/dataloaders/stereo_dataset.py b/rag_depth/src/dataloaders/stereo_dataset.py new file mode 100644 index 0000000..5ac3829 --- /dev/null +++ b/rag_depth/src/dataloaders/stereo_dataset.py @@ -0,0 +1,121 @@ +import os +import random +from torch.utils.data import Dataset +from PIL import Image +import numpy as np +from dataloaders.data_io import get_transform, read_all_lines, pfm_imread + + +class StereoDataset(Dataset): + def __init__(self, t, list_filenames, training): + self.training = training + self.t = t + self.left_filenames, self.right_filenames, self.disp_filenames, self.disp_right_filenames = self.load_path(list_filenames[self.t]) + + def load_path(self, list_filename): + lines = read_all_lines(list_filename) + splits = [line.split() for line in lines] + left_images = [x[0] for x in splits] + right_images = [x[1] for x in splits] + disp_images = [x[2] for x in splits] + if len(splits[0]) == 3: # right ground truth not available + return left_images, right_images, disp_images, None + else: + disp_right_images = [x[3] for x in splits] + return left_images, right_images, disp_images, disp_right_images + + def load_image(self, filename): + return Image.open(filename).convert('RGB') + + def load_disp(self, filename): + data, scale = pfm_imread(filename) + data = np.ascontiguousarray(data, dtype=np.float32) + return data + + def load_img_disp(self, filename): + data = Image.open(filename) + data = np.array(data, dtype=np.float32) / 256. + return data + + def __len__(self): + return len(self.left_filenames) + + def __getitem__(self, index): + + left_img = self.load_image(os.path.join(self.left_filenames[index])) + right_img = self.load_image(os.path.join(self.right_filenames[index])) + disparity = self.load_img_disp(os.path.join(self.disp_filenames[index])) + + + if self.disp_right_filenames: + disparity_right = self.load_img_disp(os.path.join(self.disp_right_filenames[index])) + else: + disparity_right = None + + + + if self.training: + w, h = left_img.size + crop_w, crop_h = 768, 384 #384, 192 + + x1 = random.randint(0, w - crop_w) + y1 = random.randint(0, h - crop_h) + + # random crop + left_img = left_img.crop((x1, y1, x1 + crop_w, y1 + crop_h)) + right_img = right_img.crop((x1, y1, x1 + crop_w, y1 + crop_h)) + disparity = disparity[y1:y1 + crop_h, x1:x1 + crop_w] + if self.disp_right_filenames: + disparity_right = disparity_right[y1:y1 + crop_h, x1:x1 + crop_w] + + # to tensor, normalize + processed = get_transform() + left_img = processed(left_img) + right_img = processed(right_img) + + if self.disp_right_filenames: + return {"left": left_img, + "right": right_img, + "disparity": disparity, + "disparity_right":disparity_right, + "left_filename": self.left_filenames[index]} + else: + return {"left": left_img, + "right": right_img, + "disparity": disparity} + else: + w, h = left_img.size + + # normalize + processed = get_transform() + left_img = processed(left_img).numpy() + right_img = processed(right_img).numpy() + + + top_pad = 480 - h + right_pad = 960 - w + assert top_pad > 0 and right_pad > 0 + + # pad images + left_img = np.lib.pad(left_img, ((0, 0), (top_pad, 0), (0, right_pad)), mode='constant', constant_values=0) + right_img = np.lib.pad(right_img, ((0, 0), (top_pad, 0), (0, right_pad)), mode='constant', + constant_values=0) + # pad disparity gt + if disparity is not None: + assert len(disparity.shape) == 2 + disparity = np.lib.pad(disparity, ((top_pad, 0), (0, right_pad)), mode='constant', constant_values=0) + + if disparity is not None: + return {"left": left_img, + "right": right_img, + "disparity": disparity, + "top_pad": top_pad, + "right_pad": right_pad, + "left_filename": self.left_filenames[index]} + else: + return {"left": left_img, + "right": right_img, + "top_pad": top_pad, + "right_pad": right_pad, + "left_filename": self.left_filenames[index], + "right_filename": self.right_filenames[index]} diff --git a/rag_depth/src/filenames/drivingstereo/drivingstereo_all_test.txt b/rag_depth/src/filenames/drivingstereo/drivingstereo_all_test.txt new file mode 100644 index 0000000..d761d84 --- /dev/null +++ b/rag_depth/src/filenames/drivingstereo/drivingstereo_all_test.txt @@ -0,0 +1,400 @@ +/data/zhangchenghao/DrivingStereoDatasets/cloudy/left-image-half-size/2018-10-31-06-55-01_2018-10-31-07-47-32-755.jpg /data/zhangchenghao/DrivingStereoDatasets/cloudy/right-image-half-size/2018-10-31-06-55-01_2018-10-31-07-47-32-755.jpg /data/zhangchenghao/DrivingStereoDatasets/cloudy/disparity-map-half-size/2018-10-31-06-55-01_2018-10-31-07-47-32-755.png /data/zhangchenghao/DrivingStereoDatasets/cloudy/disparity-map-half-size/2018-10-31-06-55-01_2018-10-31-07-47-32-755.png +/data/zhangchenghao/DrivingStereoDatasets/cloudy/left-image-half-size/2018-10-31-06-55-01_2018-10-31-07-10-34-138.jpg 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/data/zhangchenghao/DrivingStereoDatasets/sunny/depth-map-half-size/2018-10-19-09-30-39_2018-10-19-09-59-56-281.png diff --git a/rag_depth/src/models/__init__.py b/rag_depth/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rag_depth/src/models/new_model_2d.py b/rag_depth/src/models/new_model_2d.py new file mode 100644 index 0000000..8a64502 --- /dev/null +++ b/rag_depth/src/models/new_model_2d.py @@ -0,0 +1,177 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from automl.genotypes_2d import PRIMITIVES +from automl.genotypes_2d import Genotype +from automl.operations_2d import * +import torch.nn.functional as F +import numpy as np +import pdb + +class Cell(nn.Module): + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_filter_multiplier, cell_arch, network_arch, + filter_multiplier, downup_sample, args=None): + super(Cell, self).__init__() + self.cell_arch = cell_arch + + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + self.C_prev = int(block_multiplier * prev_filter_multiplier) + self.C_prev_prev = int(block_multiplier * prev_prev_fmultiplier) + self.downup_sample = downup_sample + self.pre_preprocess = ConvBR_2d(self.C_prev_prev, self.C_out, 1, 1, 0) + self.preprocess = ConvBR_2d(self.C_prev, self.C_out, 1, 1, 0) + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + if downup_sample == -1: + self.scale = 0.5 + elif downup_sample == 1: + self.scale = 2 + for x in self.cell_arch: # self.cell_arch: tensor([[0,1], [1,0], [3,0], [2,0], [8,0], [6,0]]) + primitive = PRIMITIVES[x[1]] # 'conv_3×3', 'skip_connect' + op = OPS_2d[primitive](self.C_out, stride=1) + self._ops.append(op) + + def scale_dimension(self, dim, scale): + return (int((float(dim) - 1.0) * scale + 1.0) if dim % 2 == 1 else int((float(dim) * scale))) + + def forward(self, prev_prev_input, prev_input): + s0 = prev_prev_input + s1 = prev_input + if self.downup_sample != 0: + feature_size_h = self.scale_dimension(s1.shape[2], self.scale) + feature_size_w = self.scale_dimension(s1.shape[3], self.scale) + s1 = F.interpolate(s1, [feature_size_h, feature_size_w], mode='bilinear', align_corners=True) + if (s0.shape[2] != s1.shape[2]) or (s0.shape[3] != s1.shape[3]): + s0 = F.interpolate(s0, (s1.shape[2], s1.shape[3]), + mode='bilinear', align_corners=True) + + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + s1 = self.preprocess(s1) + + states = [s0, s1] + offset = 0 + ops_index = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if branch_index in self.cell_arch[:, 0]: # [0, 1, 3, 2, 8, 6] + if prev_prev_input is None and j == 0: + ops_index += 1 + continue + new_state = self._ops[ops_index](h) + new_states.append(new_state) + ops_index += 1 + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + return prev_input, concat_feature + + +class newFeature(nn.Module): + def __init__(self, network_arch, cell_arch, cell=Cell, args=None): + super(newFeature, self).__init__() + self.args = args + self.cells = nn.ModuleList() + self.network_arch = torch.from_numpy(network_arch) + self.cell_arch = torch.from_numpy(cell_arch) + self._step = 3#args.fea_step # 3 + self._num_layers = 4#args.fea_num_layers # 4 + self._block_multiplier = 3#args.fea_block_multiplier # 3 + self._filter_multiplier = 4#args.fea_filter_multiplier # 4 + + initial_fm = self._filter_multiplier * self._block_multiplier + half_initial_fm = initial_fm // 2 + + self.stem0 = ConvBR_2d(3, half_initial_fm, 3, stride=1, padding=1) + self.stem1 = ConvBR_2d(half_initial_fm, initial_fm, 3, stride=3, padding=1) + self.stem2 = ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1) + + filter_param_dict = {0: 1, 1: 2, 2: 4, 3: 8} + + for i in range(self._num_layers): + + level_option = torch.sum(self.network_arch[i], dim=1) + prev_level_option = torch.sum(self.network_arch[i - 1], dim=1) + prev_prev_level_option = torch.sum(self.network_arch[i - 2], dim=1) + level = torch.argmax(level_option).item() + prev_level = torch.argmax(prev_level_option).item() + prev_prev_level = torch.argmax(prev_prev_level_option).item() + + if i == 0: + downup_sample = - torch.argmax(torch.sum(self.network_arch[0], dim=1)) + _cell = cell(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, + self.cell_arch, self.network_arch[i], + int(self._filter_multiplier * filter_param_dict[level]), + downup_sample, self.args) + + else: + three_branch_options = torch.sum(self.network_arch[i], dim=0) + downup_sample = torch.argmax(three_branch_options).item() - 1 + if i == 1: + _cell = cell(self._step, self._block_multiplier, + initial_fm / self._block_multiplier, + int(self._filter_multiplier * filter_param_dict[prev_level]), + self.cell_arch, self.network_arch[i], + int(self._filter_multiplier * filter_param_dict[level]), + downup_sample, self.args) + + else: + _cell = cell(self._step, self._block_multiplier, + int(self._filter_multiplier * filter_param_dict[prev_prev_level]), + int(self._filter_multiplier * filter_param_dict[prev_level]), + self.cell_arch, self.network_arch[i], + int(self._filter_multiplier * filter_param_dict[level]), downup_sample, self.args) + + self.cells += [_cell] + + self.last_3 = ConvBR_2d(initial_fm , initial_fm, 1, 1, 0, bn=False, relu=False) + self.last_6 = ConvBR_2d(initial_fm*2 , initial_fm, 1, 1, 0) + self.last_12 = ConvBR_2d(initial_fm*4 , initial_fm*2, 1, 1, 0) + self.last_24 = ConvBR_2d(initial_fm*8 , initial_fm*4, 1, 1, 0) + + def forward(self, x): + stem0 = self.stem0(x) + stem1 = self.stem1(stem0) + stem2 = self.stem2(stem1) + out = (stem1, stem2) + + for i in range(self._num_layers): + out = self.cells[i](out[0], out[1]) + + last_output = out[-1] + + h, w = stem2.size()[2], stem2.size()[3] + upsample_6 = nn.Upsample(size=stem2.size()[2:], mode='bilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[h//2, w//2], mode='bilinear', align_corners=True) + upsample_24 = nn.Upsample(size=[h//4, w//4], mode='bilinear', align_corners=True) + + if last_output.size()[2] == h: + fea = self.last_3(last_output) + elif last_output.size()[2] == h//2: + fea = self.last_3(upsample_6(self.last_6(last_output))) + elif last_output.size()[2] == h//4: + fea = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(last_output))))) + elif last_output.size()[2] == h//8: + fea = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(upsample_24(self.last_24(last_output))))))) + + return fea + + def get_params(self): + bn_params = [] + non_bn_params = [] + for name, param in self.named_parameters(): + if 'bn' in name or 'downsample.1' in name: + bn_params.append(param) + else: + bn_params.append(param) + return bn_params, non_bn_params + diff --git a/rag_depth/src/models/new_model_3d.py b/rag_depth/src/models/new_model_3d.py new file mode 100644 index 0000000..528d5da --- /dev/null +++ b/rag_depth/src/models/new_model_3d.py @@ -0,0 +1,159 @@ +import torch +import torch.nn as nn +import numpy as np +from automl.genotypes_3d import PRIMITIVES_3D +from automl.genotypes_3d import Genotype_3D +from automl.operations_3d import * +import torch.nn.functional as F +import numpy as np +import pdb + +class Cell(nn.Module): + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_filter_multiplier, cell_arch, network_arch, + filter_multiplier, downup_sample, args=None): + super(Cell, self).__init__() + self.cell_arch = cell_arch + + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + self.C_prev = int(block_multiplier * prev_filter_multiplier) + self.C_prev_prev = int(block_multiplier * prev_prev_fmultiplier) + self.downup_sample = downup_sample + self.pre_preprocess = ConvBR_3d(self.C_prev_prev, self.C_out, 1, 1, 0) + self.preprocess = ConvBR_3d(self.C_prev, self.C_out, 1, 1, 0) + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + if downup_sample == -1: + self.scale = 0.5 + elif downup_sample == 1: + self.scale = 2 + for x in self.cell_arch: + primitive = PRIMITIVES_3D[x[1]] + op = OPS_3d[primitive](self.C_out, stride=1) + self._ops.append(op) + + def scale_dimension(self, dim, scale): + return (int((float(dim) - 1.0) * scale + 1.0) if dim % 2 == 1 else int((float(dim) * scale))) + + def forward(self, prev_prev_input, prev_input): + s0 = prev_prev_input + s1 = prev_input + if self.downup_sample != 0: + feature_size_d = self.scale_dimension(s1.shape[2], self.scale) + feature_size_h = self.scale_dimension(s1.shape[3], self.scale) + feature_size_w = self.scale_dimension(s1.shape[4], self.scale) + s1 = F.interpolate(s1, [feature_size_d, feature_size_h, feature_size_w], mode='trilinear', align_corners=True) + if (s0.shape[2] != s1.shape[2]) or (s0.shape[3] != s1.shape[3]) or (s0.shape[4] != s1.shape[4]): + s0 = F.interpolate(s0, (s1.shape[2], s1.shape[3], s1.shape[4]), + mode='trilinear', align_corners=True) + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + s1 = self.preprocess(s1) + + states = [s0, s1] + offset = 0 + ops_index = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if branch_index in self.cell_arch[:, 0]: + if prev_prev_input is None and j == 0: + ops_index += 1 + continue + new_state = self._ops[ops_index](h) + new_states.append(new_state) + ops_index += 1 + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + return prev_input, concat_feature + +class newMatching(nn.Module): + def __init__(self, network_arch, cell_arch, cell=Cell, args=None): + super(newMatching, self).__init__() + self.args = args + self.cells = nn.ModuleList() + self.network_arch = torch.from_numpy(network_arch) + self.cell_arch = torch.from_numpy(cell_arch) + self._step = 3#args.mat_step + self._num_layers = 8#args.mat_num_layers # 8 + self._block_multiplier = 3#args.mat_block_multiplier # 3 + self._filter_multiplier = 4#args.mat_filter_multiplier # 4 + + initial_fm = self._filter_multiplier * self._block_multiplier + half_initial_fm = initial_fm // 2 + + self.stem0 = ConvBR_3d(initial_fm*2, initial_fm, 3, stride=1, padding=1) + self.stem1 = ConvBR_3d(initial_fm, initial_fm, 3, stride=1, padding=1) + + filter_param_dict = {0: 1, 1: 2, 2: 4, 3: 8} + for i in range(self._num_layers): + level_option = torch.sum(self.network_arch[i], dim=1) + prev_level_option = torch.sum(self.network_arch[i - 1], dim=1) + prev_prev_level_option = torch.sum(self.network_arch[i - 2], dim=1) + level = torch.argmax(level_option).item() + prev_level = torch.argmax(prev_level_option).item() + prev_prev_level = torch.argmax(prev_prev_level_option).item() + if i == 0: + downup_sample = - torch.argmax(torch.sum(self.network_arch[0], dim=1)) + _cell = cell(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, + self.cell_arch, self.network_arch[i], + self._filter_multiplier * filter_param_dict[level], + downup_sample, self.args) + + else: + three_branch_options = torch.sum(self.network_arch[i], dim=0) + downup_sample = torch.argmax(three_branch_options).item() - 1 + if i == 1: + _cell = cell(self._step, self._block_multiplier, + initial_fm / self._block_multiplier, + self._filter_multiplier * filter_param_dict[prev_level], + self.cell_arch, self.network_arch[i], + self._filter_multiplier * filter_param_dict[level], + downup_sample, self.args) + else: + _cell = cell(self._step, self._block_multiplier, + self._filter_multiplier * filter_param_dict[prev_prev_level], + self._filter_multiplier * filter_param_dict[prev_level], + self.cell_arch, self.network_arch[i], + self._filter_multiplier * filter_param_dict[level], downup_sample, self.args) + + self.cells += [_cell] + + self.last_3 = ConvBR_3d(initial_fm, 1, 3, 1, 1, bn=False, relu=False) + self.last_6 = ConvBR_3d(initial_fm*2 , initial_fm, 1, 1, 0) + self.last_12 = ConvBR_3d(initial_fm*4 , initial_fm*2, 1, 1, 0) + self.last_24 = ConvBR_3d(initial_fm*8 , initial_fm*4, 1, 1, 0) + + + def forward(self, x): + stem0 = self.stem0(x) + stem1 = self.stem1(stem0) + out = (stem0, stem1) + + for i in range(self._num_layers): + out = self.cells[i](out[0], out[1]) + last_output = out[-1] + + #define upsampling + d, h, w = x.size()[2], x.size()[3], x.size()[4] + upsample_6 = nn.Upsample(size=x.size()[2:], mode='trilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[d//2, h//2, w//2], mode='trilinear', align_corners=True) + upsample_24 = nn.Upsample(size=[d//4, h//4, w//4], mode='trilinear', align_corners=True) + + if last_output.size()[3] == h: + mat = self.last_3(last_output) + elif last_output.size()[3] == h//2: + mat = self.last_3(upsample_6(self.last_6(last_output))) + elif last_output.size()[3] == h//4: + mat = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(last_output))))) + elif last_output.size()[3] == h//8: + mat = self.last_3(upsample_6(self.last_6(upsample_12(self.last_12(upsample_24(self.last_24(last_output))))))) + return mat + diff --git a/rag_depth/src/models/rag_model.py b/rag_depth/src/models/rag_model.py new file mode 100644 index 0000000..de7a69c --- /dev/null +++ b/rag_depth/src/models/rag_model.py @@ -0,0 +1,881 @@ +import numpy as np +import torch +import torch.nn as nn + +import utils + +from copy import deepcopy + +from automl.darts_operation import * + +from automl.operations_2d import * +from automl.operations_3d import * +from automl.genotypes_2d import PRIMITIVES +from automl.genotypes_3d import PRIMITIVES_3D + + + +class DisparityRegression(nn.Module): + def __init__(self, maxdisp): + super(DisparityRegression, self).__init__() + self.maxdisp = maxdisp + + def forward(self, x): + assert(x.is_contiguous() == True) + with torch.cuda.device_of(x): + disp = torch.reshape(torch.arange(0, self.maxdisp, device=torch.cuda.current_device(), dtype=torch.float32),[1,self.maxdisp,1,1]) + disp = disp.repeat(x.size()[0], 1, x.size()[2], x.size()[3]) + out = torch.sum(x * disp, 1) + return out + + +class Disp(nn.Module): + def __init__(self, maxdisp=192): + super(Disp, self).__init__() + self.maxdisp = maxdisp + self.softmax = nn.Softmin(dim=1) + self.disparity = DisparityRegression(maxdisp=self.maxdisp) + + def forward(self, x): + x = F.interpolate(x, [self.maxdisp, x.size()[3]*3, x.size()[4]*3], mode='trilinear', align_corners=False) + x = torch.squeeze(x, 1) + x = self.softmax(x) + x = self.disparity(x) + return x + +def upsample(x, scale_factor=2, mode="bilinear", align_corners=False): + """Upsample input tensor by a factor of 2 + """ + return F.interpolate(x, scale_factor=scale_factor, mode=mode, align_corners=align_corners) + +class DispHead(nn.Module): + def __init__(self, input_dim=100): + super(DispHead, self).__init__() + # self.norm1 = nn.BatchNorm2d(input_dim) + self.conv1 = nn.Conv2d(input_dim, 1, 3, padding=1) + # self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + + def forward(self, x, scale): + # x = self.relu(self.norm1(x)) + x = self.sigmoid(self.conv1(x)) + if scale > 1: + x = upsample(x, scale_factor=scale) + return x + + +class Cell_2d(nn.Module): + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_filter_multiplier, genotype, + filter_multiplier, downup_sample): + super(Cell_2d, self).__init__() + self.genotype = genotype + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + self.C_prev = int(block_multiplier * prev_filter_multiplier) + self.C_prev_prev = int(block_multiplier * prev_prev_fmultiplier) + self.downup_sample = downup_sample + self.pre_preprocess = ConvBR_2d(self.C_prev_prev, self.C_out, 1, 1, 0) + self.preprocess = ConvBR_2d(self.C_prev, self.C_out, 1, 1, 0) + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + if downup_sample == -1: + self.scale = 0.5 + elif downup_sample == 1: + self.scale = 2 + + for x in self.genotype.normal: # self.cell_arch: tensor([[0,1], [1,0], [3,0], [2,0], [8,0], [6,0]]) + primitive = PRIMITIVES[x[1]] # 'conv_3×3', 'skip_connect' + op = OPS_2d[primitive](self.C_out, stride=1) + self._ops.append(op) + + + def scale_dimension(self, dim, scale): + return (int((float(dim) - 1.0) * scale + 1.0) if dim % 2 == 1 else int((float(dim) * scale))) + + def forward(self, prev_prev_input, prev_input): + s0 = prev_prev_input + s1 = prev_input + if self.downup_sample != 0: + feature_size_h = self.scale_dimension(s1.shape[2], self.scale) + feature_size_w = self.scale_dimension(s1.shape[3], self.scale) + s1 = F.interpolate(s1, [feature_size_h, feature_size_w], mode='bilinear', align_corners=True) + if (s0.shape[2] != s1.shape[2]) or (s0.shape[3] != s1.shape[3]): + s0 = F.interpolate(s0, (s1.shape[2], s1.shape[3]), + mode='bilinear', align_corners=True) + + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + s1 = self.preprocess(s1) + + states = [s0, s1] + offset = 0 + ops_index = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if branch_index in self.genotype.normal[:, 0]: + if prev_prev_input is None and j == 0: + ops_index += 1 + continue + new_state = self._ops[ops_index](h) + new_states.append(new_state) + ops_index += 1 + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + return prev_input, concat_feature + + +class Cell_3d(nn.Module): + def __init__(self, steps, block_multiplier, prev_prev_fmultiplier, + prev_filter_multiplier, genotype, + filter_multiplier, downup_sample): + super(Cell_3d, self).__init__() + self.genotype = genotype + self.C_in = block_multiplier * filter_multiplier + self.C_out = filter_multiplier + self.C_prev = int(block_multiplier * prev_filter_multiplier) + self.C_prev_prev = int(block_multiplier * prev_prev_fmultiplier) + self.downup_sample = downup_sample + self.pre_preprocess = ConvBR_3d(self.C_prev_prev, self.C_out, 1, 1, 0) + self.preprocess = ConvBR_3d(self.C_prev, self.C_out, 1, 1, 0) + self._steps = steps + self.block_multiplier = block_multiplier + self._ops = nn.ModuleList() + if downup_sample == -1: + self.scale = 0.5 + elif downup_sample == 1: + self.scale = 2 + for x in self.genotype.reduce: + primitive = PRIMITIVES_3D[x[1]] + op = OPS_3d[primitive](self.C_out, stride=1) + self._ops.append(op) + + + def scale_dimension(self, dim, scale): + return (int((float(dim) - 1.0) * scale + 1.0) if dim % 2 == 1 else int((float(dim) * scale))) + + def forward(self, prev_prev_input, prev_input): + s0 = prev_prev_input + s1 = prev_input + if self.downup_sample != 0: + feature_size_d = self.scale_dimension(s1.shape[2], self.scale) + feature_size_h = self.scale_dimension(s1.shape[3], self.scale) + feature_size_w = self.scale_dimension(s1.shape[4], self.scale) + s1 = F.interpolate(s1, [feature_size_d, feature_size_h, feature_size_w], mode='trilinear', align_corners=True) + if (s0.shape[2] != s1.shape[2]) or (s0.shape[3] != s1.shape[3]) or (s0.shape[4] != s1.shape[4]): + s0 = F.interpolate(s0, (s1.shape[2], s1.shape[3], s1.shape[4]), + mode='trilinear', align_corners=True) + s0 = self.pre_preprocess(s0) if (s0.shape[1] != self.C_out) else s0 + s1 = self.preprocess(s1) + + states = [s0, s1] + offset = 0 + ops_index = 0 + for i in range(self._steps): + new_states = [] + for j, h in enumerate(states): + branch_index = offset + j + if branch_index in self.genotype.reduce[:, 0]: + if prev_prev_input is None and j == 0: + ops_index += 1 + continue + new_state = self._ops[ops_index](h) + new_states.append(new_state) + ops_index += 1 + + s = sum(new_states) + offset += len(states) + states.append(s) + + concat_feature = torch.cat(states[-self.block_multiplier:], dim=1) + return prev_input, concat_feature + + + +class Network(nn.Module): + def __init__(self, genotype, device): + super(Network, self).__init__() + self.device = device + + # feature net + self.cells_2d = nn.ModuleList() + self._step = 3 + self._num_layers_2d = 4 + self._block_multiplier = 3 + self._filter_multiplier = 4 + self._K_multiplier = 2 + + initial_fm = self._filter_multiplier * self._block_multiplier + half_initial_fm = initial_fm // 2 + + self.length = {'stem_2d0': 1, 'stem_2d1': 1, 'stem_2d2': 1, 'last_3_2d': 1, 'stem_3d0': 1, 'stem_3d1': 1, 'last_3_3d': 1, 'last_6_3d': 1, 'last_12_3d': 1} + self.arch_init = {'stem_2d0':[0], 'stem_2d1':[0], 'stem_2d2':[0], 'last_3_2d':[0], 'stem_3d0':[0], 'stem_3d1':[0], 'last_3_3d':[0], 'last_6_3d':[0], 'last_12_3d':[0]} + + self.stem2d0 = nn.ModuleList([ConvBR_2d(3, half_initial_fm, 3, stride=1, padding=1)]) + self.stem2d1 = nn.ModuleList([ConvBR_2d(half_initial_fm, initial_fm, 3, stride=3, padding=1)]) + self.stem2d2 = nn.ModuleList([ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1)]) + + + #import pdb; pdb.set_trace() + # feature net structure [1, 0, 1, 0] + for i in range(self._num_layers_2d): + if i == 0: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, genotype, 8, -1) + elif i == 1: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + 8, genotype, 4, 1) + elif i == 2: + cell = Cell_2d(self._step, self._block_multiplier, 8, + 4, genotype, 8, -1) + elif i == 3: + cell = Cell_2d(self._step, self._block_multiplier, 4, + 8, genotype, 4, 1) + + + self.cells_2d.append(nn.ModuleList([cell])) + + self.arch_init['cell_2d' + str(i)] = [0] + self.length['cell_2d'+str(i)] = 1 + + self.last_3_2d = nn.ModuleList([ConvBR_2d(initial_fm , initial_fm, 1, 1, 0, bn=False, relu=False)]) + + + # matching net + self._num_layers_3d = 8 + self.cells_3d = nn.ModuleList() + + self.stem3d0 = nn.ModuleList([ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1)]) + self.stem3d1 = nn.ModuleList([ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1)]) + + # matching net structure [0, 0, 0, 1, 2, 1, 2, 2] + for i in range(self._num_layers_3d): + if i == 0: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, genotype, 4 , 0) + elif i == 1: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, 4, genotype, 4 , 0) + + elif i == 2: + cell = Cell_2d(self._step, self._block_multiplier, 4, 4, genotype, 4, 0) + + elif i == 3: + cell = Cell_2d(self._step, self._block_multiplier, 4, 4, genotype, 8, -1) + + elif i == 4: + cell = Cell_2d(self._step, self._block_multiplier, 4, 8, genotype, 16, -1) + + elif i == 5: + cell = Cell_2d(self._step, self._block_multiplier, 8, 16, genotype, 8, 1) + + elif i == 6: + cell = Cell_2d(self._step, self._block_multiplier, 16, 8, genotype, 16, -1) + + elif i == 7: + cell = Cell_2d(self._step, self._block_multiplier, 8, 16, genotype, 16, 0) + + self.cells_3d.append(nn.ModuleList([cell])) + + self.arch_init['cell_3d' + str(i)] = [0] + self.length['cell_3d'+str(i)] = 1 + + + self.last_3_3d = nn.ModuleList([ConvBR_2d(initial_fm, 1, 3, 1, 1, bn=False, relu=False)]) + self.last_6_3d = nn.ModuleList([ConvBR_2d(initial_fm*2 , initial_fm, 1, 1, 0)]) + self.last_12_3d = nn.ModuleList([ConvBR_2d(initial_fm*4 , initial_fm*2, 1, 1, 0)]) + + + self.maxdisp = 192 + self.disp = Disp(self.maxdisp) + + self.depth_head = DispHead(input_dim=1) + self.max_depth = 80 + + + # parameter for architecture search + self.p = None + # The new models, model to train + self.new_models = None + self.model_to_train = None + + def feature(self, x, task_arch, path): + + arch_stem_2d0 = None + arch_stem_2d1 = None + arch_stem_2d2 = None + arch_last_3_2d = None + if task_arch is not None: + arch_stem_2d0 = task_arch['stem_2d0'][0] + arch_stem_2d1 = task_arch['stem_2d1'][0] + arch_stem_2d2 = task_arch['stem_2d2'][0] + arch_last_3_2d = task_arch['last_3_2d'][0] + elif path is not None: + arch_stem = path[0] + + stem0 = self.stem2d0[arch_stem_2d0](x) + stem1 = self.stem2d1[arch_stem_2d1](stem0) + stem2 = self.stem2d2[arch_stem_2d2](stem1) + out = (stem1, stem2) + + + + for i, cell in enumerate(self.cells_2d): + arch_cell = None + if task_arch is not None: + arch_cell = task_arch['cell_2d'+str(i)][0] + elif path is not None: + arch_cell = path[i+1] + out = cell[arch_cell](out[0], out[1]) + + last_output = out[-1] + + h, w = stem2.size()[2], stem2.size()[3] + if last_output.size()[2] == h: + fea = self.last_3_2d[arch_last_3_2d](last_output) + else: + print('this is a bug') + print(x.shape, last_output.size()[2], stem2.size()[2]) + + return fea + + def matching(self, x, task_arch, path): + arch_stem_3d0 = None + arch_stem_3d1 = None + arch_last_3_3d = None + arch_last_6_3d = None + arch_last_12_3d = None + if task_arch is not None: + arch_stem_3d0 = task_arch['stem_3d0'][0] + arch_stem_3d1 = task_arch['stem_3d1'][0] + arch_last_3_3d = task_arch['last_3_3d'][0] + arch_last_6_3d = task_arch['last_6_3d'][0] + arch_last_12_3d = task_arch['last_12_3d'][0] + + elif path is not None: + arch_stem = path[0] + + + stem0 = self.stem3d0[arch_stem_3d0](x) + stem1 = self.stem3d1[arch_stem_3d1](stem0) + out = (stem0, stem1) + + for i, cell in enumerate(self.cells_3d): + arch_cell = None + if task_arch is not None: + arch_cell = task_arch['cell_3d'+str(i)][0] + elif path is not None: + arch_cell = path[i+1] + out = cell[arch_cell](out[0], out[1]) + + last_output = out[-1] + + h, w = x.size()[2], x.size()[3] + upsample_6 = nn.Upsample(size=x.size()[2:], mode='bilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[h//2, w//2], mode='bilinear', align_corners=True) + + if last_output.size()[2] == h: + mat = self.last_3_3d[arch_last_3_3d](last_output) + elif last_output.size()[2] == h//2: + mat = self.last_3_3d[arch_last_3_3d](upsample_6(self.last_6_3d[arch_last_6_3d](last_output))) + elif last_output.size()[2] == h//4: + mat = self.last_3_3d[arch_last_3_3d](upsample_6(self.last_6_3d[arch_last_6_3d](upsample_12(self.last_12_3d[arch_last_12_3d](last_output))))) + return mat + + + def forward(self, left, right, t, task_arch=None, path=None): + + + x = self.feature(left, task_arch, path) + #y = self.feature(right, task_arch, path) + + ''' + with torch.cuda.device_of(x): + cost = x.new().resize_(x.size()[0], x.size()[1]*2, int(self.maxdisp/3), x.size()[2], x.size()[3]).zero_() + for i in range(int(self.maxdisp/3)): + if i > 0 : + cost[:,:x.size()[1], i,:,i:] = x[:,:,:,i:] + cost[:,x.size()[1]:, i,:,i:] = y[:,:,:,:-i] + else: + cost[:,:x.size()[1],i,:,i:] = x + cost[:,x.size()[1]:,i,:,i:] = y + ''' + cost = x + + cost = self.matching(cost, task_arch, path) + #disp = self.disp(cost) + + depth = self.depth_head(cost, 3) + + disp = torch.squeeze(depth, 1) * self.max_depth + return disp + + + ## both FeatureNet and MatchingNet are searched + def expand(self, t, genotype, device='cuda'): + # expand the network to a super model + # 0 clean the probability + self.p = [] + # 1 expand stem + # 1.1 reuse: reuse blocks + # 1.2 new: create a new block + initial_fm = self._filter_multiplier * self._block_multiplier + half_initial_fm = initial_fm // 2 + + + # 1.3 generate action parameter + self.stem2d0.append(ConvBR_2d(3, half_initial_fm, 3, stride=1, padding=1).to(device)) + num_l = self.length['stem_2d0'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['stem_2d0'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) + + self.stem2d1.append(ConvBR_2d(half_initial_fm, initial_fm, 3, stride=3, padding=1).to(device)) + num_l = self.length['stem_2d1'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['stem_2d1'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) + + self.stem2d2.append(ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1).to(device)) + num_l = self.length['stem_2d2'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['stem_2d2'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 0,1,2 + + # 2 expand cells + for i in range(self._num_layers_2d): + # 2.1 reuse: reuse blocks + # 2.2 new: create new block according to the new cell + if i == 0: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, genotype, 8, -1).to(device) + elif i == 1: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + 8, genotype, 4, 1).to(device) + elif i == 2: + cell = Cell_2d(self._step, self._block_multiplier, 8, + 4, genotype, 8, -1).to(device) + elif i == 3: + cell = Cell_2d(self._step, self._block_multiplier, 4, + 8, genotype, 4, 1).to(device) + + self.cells_2d[i].append(cell) + num_l = self.length['cell_2d'+str(i)] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['cell_2d'+str(i)] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 3,4,5,6 + + + self.last_3_2d.append(ConvBR_2d(initial_fm , initial_fm, 1, 1, 0, bn=False, relu=False).to(device)) + num_l = self.length['last_3_2d'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['last_3_2d'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 7 + + + + self.stem3d0.append(ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1).to(device)) + num_l = self.length['stem_3d0'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['stem_3d0'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 8 + + self.stem3d1.append(ConvBR_2d(initial_fm, initial_fm, 3, stride=1, padding=1).to(device)) + num_l = self.length['stem_3d1'] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['stem_3d1'] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 9 + + + for i in range(self._num_layers_3d): + if i == 0: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, + initial_fm / self._block_multiplier, genotype, 4 , 0).to(device) + elif i == 1: + cell = Cell_2d(self._step, self._block_multiplier, initial_fm / self._block_multiplier, 4, genotype, 4 , 0).to(device) + + elif i == 2: + cell = Cell_2d(self._step, self._block_multiplier, 4, 4, genotype, 4, 0).to(device) + + elif i == 3: + cell = Cell_2d(self._step, self._block_multiplier, 4, 4, genotype, 8, -1).to(device) + + elif i == 4: + cell = Cell_2d(self._step, self._block_multiplier, 4, 8, genotype, 16, -1).to(device) + + elif i == 5: + cell = Cell_2d(self._step, self._block_multiplier, 8, 16, genotype, 8, 1).to(device) + + elif i == 6: + cell = Cell_2d(self._step, self._block_multiplier, 16, 8, genotype, 16, -1).to(device) + + elif i == 7: + cell = Cell_2d(self._step, self._block_multiplier, 8, 16, genotype, 16, 0).to(device) + + self.cells_3d[i].append(cell) + + # 2.3 generate action parameter + # for each cell: reuse + create + num_l = self.length['cell_3d'+str(i)] + 1 + temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['cell_3d'+str(i)] + 1)) + temp[:num_l-1] *= self._K_multiplier + self.p.append(temp) # self.p 10,11,12,13,14,15,16,17 + + + self.last_3_3d.append(ConvBR_2d(initial_fm, 1, 3, 1, 1, bn=False, relu=False).to(device)) + #num_l = self.length['last_3_3d'] + 1 + #temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['last_3_3d'] + 1)) + #temp[:num_l-1] *= self._K_multiplier + #self.p.append(temp) # self.p 18 + + self.last_6_3d.append(ConvBR_2d(initial_fm*2 , initial_fm, 1, 1, 0).to(device)) + #num_l = self.length['last_6_3d'] + 1 + #temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['last_6_3d'] + 1)) + #temp[:num_l-1] *= self._K_multiplier + #self.p.append(temp) # self.p 19 + + self.last_12_3d.append(ConvBR_2d(initial_fm*4 , initial_fm*2, 1, 1, 0).to(device)) + #num_l = self.length['last_12_3d'] + 1 + #temp = torch.full((num_l,), 1 / (self._K_multiplier * self.length['last_12_3d'] + 1)) + #temp[:num_l-1] *= self._K_multiplier + #self.p.append(temp) # self.p 20 + + #import pdb; pdb.set_trace() + + # 3 get the new modules + self.get_new_model(t=t) + + + def get_new_model(self, t): + + # get new model (update and search) + new_models = {'stem_2d0': [], 'stem_2d1': [], 'stem_2d2': [], 'last_3_2d': [], 'stem_3d0': [], 'stem_3d1': [], 'last_3_3d': [], 'last_6_3d': [], 'last_12_3d': []} + # 1 stem & last + #new_models['stem_2d0'].append(t) + new_models['stem_2d0'] = [self.length['stem_2d0']] + new_models['stem_2d1'] = [self.length['stem_2d1']] + new_models['stem_2d2'] = [self.length['stem_2d2']] + new_models['last_3_2d'] = [self.length['last_3_2d']] + new_models['stem_3d0'] = [self.length['stem_3d0']] + new_models['stem_3d1'] = [self.length['stem_3d1']] + #new_models['last_3_3d'] = [self.length['last_3_3d']] + #new_models['last_6_3d'] = [self.length['last_6_3d']] + #new_models['last_12_3d'] = [self.length['last_12_3d']] + new_models['last_3_3d'].append(t) + new_models['last_6_3d'].append(t) + new_models['last_12_3d'].append(t) + + # 2 cells_2d & cells_3d + for i in range(self._num_layers_2d): + new_models['cell_2d' + str(i)] = [self.length['cell_2d' + str(i)]] + + for i in range(self._num_layers_3d): + new_models['cell_3d' + str(i)] = [self.length['cell_3d' + str(i)]] + + self.new_models = new_models + + def get_param(self, models): + params = [] + if 'stem_2d0' in models.keys(): + for idx in models['stem_2d0']: + params.append({'params': self.stem2d0[idx].parameters()}) + if 'stem_2d1' in models.keys(): + for idx in models['stem_2d1']: + params.append({'params': self.stem2d1[idx].parameters()}) + if 'stem_2d2' in models.keys(): + for idx in models['stem_2d2']: + params.append({'params': self.stem2d2[idx].parameters()}) + if 'last_3_2d' in models.keys(): + for idx in models['last_3_2d']: + params.append({'params': self.last_3_2d[idx].parameters()}) + if 'stem_3d0' in models.keys(): + for idx in models['stem_3d0']: + params.append({'params': self.stem3d0[idx].parameters()}) + if 'stem_3d1' in models.keys(): + for idx in models['stem_3d1']: + params.append({'params': self.stem3d1[idx].parameters()}) + if 'last_3_3d' in models.keys(): + for idx in models['last_3_3d']: + params.append({'params': self.last_3_3d[idx].parameters()}) + if 'last_6_3d' in models.keys(): + for idx in models['last_6_3d']: + params.append({'params': self.last_6_3d[idx].parameters()}) + if 'last_12_3d' in models.keys(): + for idx in models['last_12_3d']: + params.append({'params': self.last_12_3d[idx].parameters()}) + + + for i in range(self._num_layers_2d): + if 'cell_2d' + str(i) in models.keys(): + for idx in models['cell_2d' + str(i)]: + params.append({'params': self.cells_2d[i][idx].parameters()}) + + for i in range(self._num_layers_3d): + if 'cell_3d' + str(i) in models.keys(): + for idx in models['cell_3d' + str(i)]: + params.append({'params': self.cells_3d[i][idx].parameters()}) + + + return params + + def modify_param(self, models, requires_grad=True): + if 'stem_2d0' in models.keys(): + for idx in models['stem_2d0']: + utils.modify_model(self.stem2d0[idx], requires_grad) + if 'stem_2d1' in models.keys(): + for idx in models['stem_2d1']: + utils.modify_model(self.stem2d1[idx], requires_grad) + if 'stem_2d2' in models.keys(): + for idx in models['stem_2d2']: + utils.modify_model(self.stem2d2[idx], requires_grad) + + for i in range(self._num_layers_2d): + if 'cell_2d' + str(i) in models.keys(): + for idx in models['cell_2d' + str(i)]: + utils.modify_model(self.cells_2d[i][idx], requires_grad) + + if 'last_3_2d' in models.keys(): + for idx in models['last_3_2d']: + utils.modify_model(self.last_3_2d[idx], requires_grad) + + + if 'stem_3d0' in models.keys(): + for idx in models['stem_3d0']: + utils.modify_model(self.stem3d0[idx], requires_grad) + if 'stem_3d1' in models.keys(): + for idx in models['stem_3d1']: + utils.modify_model(self.stem3d1[idx], requires_grad) + if 'last_3_3d' in models.keys(): + for idx in models['last_3_3d']: + utils.modify_model(self.last_3_3d[idx], requires_grad) + if 'last_6_3d' in models.keys(): + for idx in models['last_6_3d']: + utils.modify_model(self.last_6_3d[idx], requires_grad) + if 'last_12_3d' in models.keys(): + for idx in models['last_12_3d']: + utils.modify_model(self.last_12_3d[idx], requires_grad) + + + for i in range(self._num_layers_3d): + if 'cell_3d' + str(i) in models.keys(): + for idx in models['cell_3d' + str(i)]: + utils.modify_model(self.cells_3d[i][idx], requires_grad) + + + def search_feature(self, x, selected_ops): + + stem0 = self.stem2d0[selected_ops[0]](x) + stem1 = self.stem2d1[selected_ops[1]](stem0) + stem2 = self.stem2d2[selected_ops[2]](stem1) + out = (stem1, stem2) + + for i, cell in enumerate(self.cells_2d): + out = cell[selected_ops[i+3]](out[0], out[1]) + + last_output = out[-1] + + h, w = stem2.size()[2], stem2.size()[3] + if last_output.size()[2] == h: + fea = self.last_3_2d[selected_ops[7]](last_output) + else: + print('this is a bug') + print(x.shape, last_output.size()[2], stem2.size()[2]) + + return fea + + + def search_matching(self, x, selected_ops, t): + + stem0 = self.stem3d0[selected_ops[8]](x) # x:[B,12,64,128] + stem1 = self.stem3d1[selected_ops[9]](stem0) + out = (stem0, stem1) + + for i, cell in enumerate(self.cells_3d): + out = cell[selected_ops[i+10]](out[0], out[1]) + + last_output = out[-1] + + h, w = x.size()[2], x.size()[3] + upsample_6 = nn.Upsample(size=x.size()[2:], mode='bilinear', align_corners=True) + upsample_12 = nn.Upsample(size=[h//2, w//2], mode='bilinear', align_corners=True) + + if last_output.size()[2] == h: + mat = self.last_3_3d[t](last_output) + elif last_output.size()[2] == h//2: + mat = self.last_3_3d[t](upsample_6(self.last_6_3d[t](last_output))) + elif last_output.size()[2] == h//4: + mat = self.last_3_3d[t](upsample_6(self.last_6_3d[t](upsample_12(self.last_12_3d[t](last_output))))) + return mat + + + def search_forward(self, left, right, t, selected_ops): + + + x = self.search_feature(left, selected_ops) + #y = self.search_feature(right, selected_ops) + + ''' + with torch.cuda.device_of(x): + cost = x.new().resize_(x.size()[0], x.size()[1]*2, int(self.maxdisp/3), x.size()[2], x.size()[3]).zero_() + for i in range(int(self.maxdisp/3)): + if i > 0 : + cost[:,:x.size()[1], i,:,i:] = x[:,:,:,i:] + cost[:,x.size()[1]:, i,:,i:] = y[:,:,:,:-i] + else: + cost[:,:x.size()[1],i,:,i:] = x + cost[:,x.size()[1]:,i,:,i:] = y + ''' + cost = x + cost = self.search_matching(cost, selected_ops, t) + #disp = self.disp(cost) + + depth = self.depth_head(cost, 3) + + disp = torch.squeeze(depth, 1) * self.max_depth + + + return disp + + + def select(self, t): + + model_to_train = {'stem_2d0': [], 'stem_2d1': [], 'stem_2d2': [], 'last_3_2d': [], 'stem_3d0': [], 'stem_3d1': [], 'last_3_3d': [], 'last_6_3d': [], 'last_12_3d': []} + best_archi = {'stem_2d0': [], 'stem_2d1': [], 'stem_2d2': [], 'last_3_2d': [], 'stem_3d0': [], 'stem_3d1': [], 'last_3_3d': [], 'last_6_3d': [], 'last_12_3d': []} + # stem & last + #model_to_train['stem_2d0'].append(t) + #best_archi['stem_2d0'].append(t) + + v, idx = torch.max(self.p[0], dim=0) + c = self.length['stem_2d0'] + if idx < c: # reuse + best_archi['stem_2d0'].append(idx) + elif idx == c: # update + best_archi['stem_2d0'].append(c) + model_to_train['stem_2d0'].append(c) + # 2.2 delete for stem + if idx != c: + del self.stem2d0[c] + # 2.3 update the length + self.length['stem_2d0'] = len(self.stem2d0) + + v, idx = torch.max(self.p[1], dim=0) + c = self.length['stem_2d1'] + if idx < c: + best_archi['stem_2d1'].append(idx) + elif idx == c: + best_archi['stem_2d1'].append(c) + model_to_train['stem_2d1'].append(c) + if idx != c: + del self.stem2d1[c] + self.length['stem_2d1'] = len(self.stem2d1) + + v, idx = torch.max(self.p[2], dim=0) + c = self.length['stem_2d2'] + if idx < c: + best_archi['stem_2d2'].append(idx) + elif idx == c: + best_archi['stem_2d2'].append(c) + model_to_train['stem_2d2'].append(c) + if idx != c: + del self.stem2d2[c] + self.length['stem_2d2'] = len(self.stem2d2) + + + # cells layer + for i in range(self._num_layers_2d): + name = 'cell_2d' + str(i) + model_to_train[name] = [] + best_archi[name] = [] + + v, idx = torch.max(self.p[i+3], dim=0) + c = self.length[name] + # 3.1 select the best architecture for cell + if idx != c: # reuse blocks + best_archi[name].append(idx) + elif idx == c: # create new block + best_archi[name].append(c) + model_to_train[name].append(c) + # 3.2 delete for cell + if idx != c: + del self.cells_2d[i][c] + + # 3.3 update the length + self.length[name] = len(self.cells_2d[i]) + + + v, idx = torch.max(self.p[7], dim=0) + c = self.length['last_3_2d'] + if idx < c: + best_archi['last_3_2d'].append(idx) + elif idx == c: + best_archi['last_3_2d'].append(c) + model_to_train['last_3_2d'].append(c) + if idx != c: + del self.last_3_2d[c] + self.length['last_3_2d'] = len(self.last_3_2d) + + v, idx = torch.max(self.p[8], dim=0) + c = self.length['stem_3d0'] + if idx < c: # reuse + best_archi['stem_3d0'].append(idx) + elif idx == c: # update + best_archi['stem_3d0'].append(c) + model_to_train['stem_3d0'].append(c) + # 2.2 delete for stem + if idx != c: + del self.stem3d0[c] + # 2.3 update the length + self.length['stem_3d0'] = len(self.stem3d0) + + v, idx = torch.max(self.p[9], dim=0) + c = self.length['stem_3d1'] + if idx < c: + best_archi['stem_3d1'].append(idx) + elif idx == c: + best_archi['stem_3d1'].append(c) + model_to_train['stem_3d1'].append(c) + if idx != c: + del self.stem3d1[c] + self.length['stem_3d1'] = len(self.stem3d1) + + + for i in range(self._num_layers_3d): + name = 'cell_3d' + str(i) + model_to_train[name] = [] + best_archi[name] = [] + + v, idx = torch.max(self.p[i+10], dim=0) + c = self.length[name] + # 3.1 select the best architecture for cell + if idx != c: # reuse blocks + best_archi[name].append(idx) + elif idx == c: # create new block + best_archi[name].append(c) + model_to_train[name].append(c) + # 3.2 delete for cell + if idx != c: + del self.cells_3d[i][c] + + # 3.3 update the length + self.length[name] = len(self.cells_3d[i]) + + + model_to_train['last_3_3d'].append(t) + best_archi['last_3_3d'].append(t) + + model_to_train['last_6_3d'].append(t) + best_archi['last_6_3d'].append(t) + + model_to_train['last_12_3d'].append(t) + best_archi['last_12_3d'].append(t) + + + # 5 update the model to train + self.model_to_train = model_to_train + + return best_archi diff --git a/rag_depth/src/run.py b/rag_depth/src/run.py new file mode 100644 index 0000000..878f89f --- /dev/null +++ b/rag_depth/src/run.py @@ -0,0 +1,273 @@ +import sys, os, argparse, time +import logging +import json +import numpy as np +import torch +from torch.utils.tensorboard.writer import SummaryWriter + +import utils +from dataloaders.stereo_dataset import StereoDataset + +from approaches import rag as approach + +tstart=time.time() + +# Arguments +parser=argparse.ArgumentParser(description='xxx') +parser.add_argument('--seed', type=int, default=0, help='(default=%(default)d)') +parser.add_argument('--experiment', default='drivingstereo', type=str,help='(default=%(default)s)') +parser.add_argument('--approach', default='rag', type=str,help='(default=%(default)s)') +# mode: training or search the best hyper-parameter +parser.add_argument('--mode', default='train', type=str, required=False, choices=['train', 'search'], + help='(default=%(default)s)') +# if debug is true, only use a small dataset +parser.add_argument('--debug', default='False', type=str, required=False, choices=['False', 'True'], + help='(default=%(default)s)') +# model: the basic model +parser.add_argument('--output',default='', type=str, required=False, help='(default=%(default)s)') +parser.add_argument('--device', type=str, default='0', help='choose the device') +parser.add_argument('--id', type=str, default='0', help='the id of experiment') +#parser.add_argument('--search_layers', default=4, type=int, required=False, help='(default=%(default)d)') +#parser.add_argument('--eval_layers', default=6, type=int, required=False, help='(default=%(default)d)') +# hyper parameters in cell search stage +parser.add_argument('--c_epochs', default=100, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--c_batch', default=8, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--c_lr', default=0.025, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--c_lr_a', default=0.01, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--c_lamb', default=0.0003, type=float, required=False, help='(default=%(default)f)') + +# hyper parameters in operation search stage +parser.add_argument('--o_epochs', default=100, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--o_batch', default=6, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--o_lr', default=0.025, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--o_lr_a', default=0.01, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--o_lamb', default=0.0003, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--o_lamb_a', default=0.0003, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--o_lamb_size', default=1, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--o_size', default=0, type=int, required=False, +help="the initial number of epochs for previous units") + +# hyper parameters in training stage +parser.add_argument('--epochs', default=300, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--batch', default=4, type=int, required=False, help='(default=%(default)d)') +parser.add_argument('--lr', default=0.025, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--lamb', default=0.0003, type=float, required=False, help='(default=%(default)f)') +parser.add_argument('--lamb_ewc', default=10000, type=float, required=False, help='(default=%(default)f)') + + +parser.add_argument('--maxdisp', default=192, type=float, required=False, help='(default=%(default)f)') + +args = parser.parse_args() + +if args.output == '': + args.output = '../res/'+args.experiment+'_'+args.approach+'_'+str(args.seed)+'_'+args.id+'.txt' + +print('='*100) +print('Arguments =') +for arg in vars(args): + print('\t'+arg+':', getattr(args, arg)) +print('='*100) + +######################################################################################################################## + +# Seed +np.random.seed(args.seed) +torch.manual_seed(args.seed) +if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + device = torch.device('cuda:'+args.device) +else: + device = torch.device('cpu') + print('[CUDA unavailable]') + sys.exit() + +######################################################################################################################## +# define logger +logger = logging.getLogger() + +# define tensorboard writer +exp_name = args.experiment+'_'+args.approach+'_'+str(args.seed)+'_'+args.id + +writer = SummaryWriter(log_dir='../logs/' + exp_name) + + +# Load date +print('Load data...') + +# logging the experiment config +config_exp = ["name: {}".format(exp_name), + "mode: {}".format(args.mode), + "dataset: {}".format(args.experiment), + "approach: {}".format(args.approach), + "device: {}".format(args.device), + "id: {}".format(args.id)] +for i, string in enumerate(config_exp): + writer.add_text("Config_Experiment", string, i) +# logging the hyperparameter config +config_hyper_train = [ + "train_epochs: {}".format(args.epochs), + "train_batch_size: {}".format(args.batch), + "train_learning_rate: {}".format(args.lr), + "train_weight_decay: {}".format(args.lamb) +] +# "learning_rate_patience: {}".format(args.lr_patience), +# "learning_factor: {}".format(args.lr_factor), + + +config_hyper_operation = [ + "operation_search_epochs: {}".format(args.o_epochs), + "operation_search_batch_size: {}".format(args.o_batch), + "operation_search_learning_rate_m: {}".format(args.o_lr), + "operation_search_learning_rate_p: {}".format(args.o_lr_a), + "operation_search_weight_decay_m: {}".format(args.o_lamb), + "operation_search_weight_decay_p: {}".format(args.o_lamb_a), + "operation_search_initial_epochs: {}".format(args.o_size) +] + +config_hyper_cell = [ + "cell_search_epochs: {}".format(args.c_epochs), + "cell_search_batch_size: {}".format(args.c_batch), + "cell_search_learning_rate_m: {}".format(args.c_lr), + "cell_search_learning_rate_p: {}".format(args.c_lr_a), + "cell_search_weight_decay_m: {}".format(args.c_lamb), +] + +for i, string in enumerate(config_hyper_train): + writer.add_text("Config_Train", string, i) + +for i, string in enumerate(config_hyper_operation): + writer.add_text("Config_Operation", string, i) + +for i, string in enumerate(config_hyper_cell): + writer.add_text("Config_Cell", string, i) + +# Inits +appr = approach.Appr(device=device, writer=writer, exp_name=exp_name, args=args) + + +# Loop tasks +abs_rel = np.zeros((4, 4), dtype=np.float32) +lss = np.zeros((4, 4), dtype=np.float32) +log10 = np.zeros((4, 4), dtype=np.float32) +rmse = np.zeros((4, 4), dtype=np.float32) +delta1 = np.zeros((4, 4), dtype=np.float32) +delta2 = np.zeros((4, 4), dtype=np.float32) +delta3 = np.zeros((4, 4), dtype=np.float32) +model_size = [] + + +train_data_list = ['./filenames/drivingstereo/drivingstereo_cloudy_train.txt', './filenames/drivingstereo/drivingstereo_foggy_train.txt', + './filenames/drivingstereo/drivingstereo_rainy_train.txt', './filenames/drivingstereo/drivingstereo_sunny_train.txt'] +test_data_list = ['./filenames/drivingstereo/drivingstereo_cloudy_test.txt', './filenames/drivingstereo/drivingstereo_foggy_test.txt', + './filenames/drivingstereo/drivingstereo_rainy_test.txt', './filenames/drivingstereo/drivingstereo_sunny_test.txt'] + + + + +for t in range(len(train_data_list)): + print('*'*100) + print('Task {:2d}'.format(t)) + print('*'*100) + + # get dataset + train_data = StereoDataset(t, train_data_list, True) + valid_data = StereoDataset(t, train_data_list, False) + + # Train + appr.train(t, train_data, valid_data, device=device) + print('-'*100) + + # Test + for u in range(t+1): + test_data = StereoDataset(u, test_data_list, False) + test_loader = torch.utils.data.DataLoader( + test_data, batch_size=1, shuffle=False, pin_memory=True, num_workers=4) + + test_loss, test_abs, test_log10, test_rmse, test_a1, test_a2, test_a3 = appr.eval(u, test_loader, mode='train', device=device) + print('>>> Test on task {:2d} - {:15s}: loss={:.3f}, abs_rel={:.3f}, log10={:.3f}, rmse={:.3f}, delta1={:.3f}, delta2={:.3f}, delta3={:.3f} <<<'.format( + u, 'Scene Flow', test_loss, test_abs, test_log10, test_rmse, test_a1, test_a2, test_a3)) + writer.add_scalars('Test/Loss', + {'task{}'.format(u): test_loss}, global_step=t) + writer.add_scalars('Test/Abs_Rel', + {'task{}'.format(u): test_abs}, global_step=t) + writer.add_scalars('Test/RMSE', + {'task{}'.format(u): test_rmse}, global_step=t) + + abs_rel[t, u] = test_abs + lss[t, u] = test_loss + log10[t, u] = test_log10 + rmse[t, u] = test_rmse + delta1[t, u] = test_a1 + delta2[t, u] = test_a2 + delta3[t, u] = test_a3 + + checkpoint_data = {'task': t, 'model': appr.model.state_dict(), 'optimizer': appr.optimizer.state_dict()} + save_path = "../logs/" + exp_name + "/checkpoint_task" + str(t) + ".ckpt" + torch.save(checkpoint_data, save_path) + + model_size.append(utils.get_model_size(appr.model, mode='M')) + writer.add_scalars('ModelParameter(M)', + {'ModelParameter(M)': utils.get_model_size(appr.model, 'M')}, + global_step=t) + +# Done, logging the experiment results +print('*'*100) +print('Abs_Rel =') +for i in range(abs_rel.shape[0]): + print('\t', end='') + for j in range(abs_rel.shape[1]): + writer.add_text("Results/Abs_Rel", '{:.3f} '.format(abs_rel[i, j]), i) + print('{:.3f} '.format(abs_rel[i, j]), end='') + print() +print('*'*100) +print('Done!') + +print('Log10 =') +for i in range(log10.shape[0]): + print('\t', end='') + for j in range(log10.shape[1]): + writer.add_text("Results/Log10", '{:.3f} '.format(log10[i, j]), i) + print('{:.3f} '.format(log10[i, j]), end='') + print() +print('*'*100) +print('Done!') + +print('RMSE =') +for i in range(rmse.shape[0]): + print('\t', end='') + for j in range(rmse.shape[1]): + writer.add_text("Results/RMSE", '{:.3f} '.format(rmse[i, j]), i) + print('{:.3f} '.format(rmse[i, j]), end='') + print() +print('*'*100) +print('Done!') + +print('Delta_1 =') +for i in range(delta1.shape[0]): + print('\t', end='') + for j in range(delta1.shape[1]): + writer.add_text("Results/Delta_1", '{:.3f} '.format(delta1[i, j]), i) + print('{:.3f} '.format(delta1[i, j]), end='') + print() +print('*'*100) +print('Done!') + +print('Delta_2 =') +for i in range(delta2.shape[0]): + print('\t', end='') + for j in range(delta2.shape[1]): + writer.add_text("Results/Delta_2", '{:.3f} '.format(delta2[i, j]), i) + print('{:.3f} '.format(delta2[i, j]), end='') + print() +print('*'*100) +print('Done!') + +print('Delta_3 =') +for i in range(delta3.shape[0]): + print('\t', end='') + for j in range(delta3.shape[1]): + writer.add_text("Results/Delta_3", '{:.3f} '.format(delta3[i, j]), i) + print('{:.3f} '.format(delta3[i, j]), end='') + print() +print('*'*100) +print('Done!') diff --git a/rag_depth/src/utils.py b/rag_depth/src/utils.py new file mode 100644 index 0000000..2638fa5 --- /dev/null +++ b/rag_depth/src/utils.py @@ -0,0 +1,219 @@ +import os,sys +import math +import numpy as np +from copy import deepcopy +import torch +from tqdm import tqdm + + +def get_model_size(model, mode=None): + count = 0 + for p in model.parameters(): + count += np.prod(p.size()) + human_count = None + if mode is None: + human_count = human_format(count) + elif mode == 'M': + human_count = human_format_m(count) + + return human_count + + +def print_model_report(model): + print('-'*100) + print(model) + print('Dimensions =', end=' ') + count = 0 + for p in model.parameters(): + print(p.size(), end=' ') + count += np.prod(p.size()) + print() + print('Num parameters = %s' % (human_format(count))) + print('-'*100) + return count + + +def human_format_m(num): + + return num / 1000000.0 + + +def human_format(num): + magnitude = 0 + while abs(num) >= 1000: + magnitude += 1 + num /= 1000.0 + return '%.2f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude]) + + +def print_optimizer_config(optim): + if optim is None: + print(optim) + else: + print(optim,'=',end=' ') + opt=optim.param_groups[0] + for n in opt.keys(): + if not n.startswith('param'): + print(n+':',opt[n],end=', ') + print() + return + +######################################################################################################################## + + +def get_model(model): + return deepcopy(model.state_dict()) + + +def set_model_(model,state_dict): + model.load_state_dict(deepcopy(state_dict)) + return + + +def modify_model(model, requires_grad=True): + for param in model.parameters(): + param.requires_grad = requires_grad + + +def freeze_model(model): + for param in model.parameters(): + param.requires_grad = False + return + + +def unfreeze_model(model): + for param in model.parameters(): + param.requires_grad = True + return + + +def freeze_parameter(params): + for param in params.values(): + param.requires_grad = False + + +def unfreeze_parameter(params): + for param in params.values(): + param.requires_grad = True + +######################################################################################################################## + + +def model_size(model): + total_size = 0.0 + for param in model.parameters(): + size_list = param.size() + size = 1 + for i in range(len(size_list)): + size *= size_list[i] + total_size += size + log_size = math.log(1+total_size, 10) + + return log_size +######################################################################################################################## + + +def compute_conv_output_size(Lin,kernel_size,stride=1,padding=0,dilation=1): + return int(np.floor((Lin+2*padding-dilation*(kernel_size-1)-1)/float(stride)+1)) + +######################################################################################################################## + + +def compute_mean_std_dataset(dataset): + # dataset already put ToTensor + mean=0 + std=0 + loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False) + for image, _ in loader: + mean+=image.mean(3).mean(2) + mean /= len(dataset) + + mean_expanded=mean.view(mean.size(0),mean.size(1),1,1).expand_as(image) + for image, _ in loader: + std+=(image-mean_expanded).pow(2).sum(3).sum(2) + + std=(std/(len(dataset)*image.size(2)*image.size(3)-1)).sqrt() + + return mean, std + +######################################################################################################################## + + +def fisher_matrix_diag(t, train_loader, model, criterion, device, sbatch=20): + # Init + fisher = {} + with torch.no_grad(): + for n, p in model.named_parameters(): + fisher[n] = 0 * p.data + # Compute + model.train() + count = 0 + for sample in train_loader: + imgL, imgR, disp_gt = sample['left'].to(device), sample['right'].to(device), sample['disparity'].to(device) + # Forward and backward + print(count) + count += 1 + model.zero_grad() + disp_est = model.forward(imgL, imgR) + mask = (disp_gt < 192) & (disp_gt > 0) + loss = criterion(t, disp_est[mask], disp_gt[mask]) + loss.backward() + + # Get gradients + with torch.no_grad(): + for n, p in model.named_parameters(): + if p.grad is not None: + fisher[n] += sbatch * p.grad.data.pow(2) + # Mean + for n, _ in model.named_parameters(): + fisher[n] = fisher[n] / len(train_loader) + + return fisher + +######################################################################################################################## + + +def cross_entropy(outputs,targets,exp=1,size_average=True,eps=1e-5): + out=torch.nn.functional.softmax(outputs) + tar=torch.nn.functional.softmax(targets) + if exp!=1: + out=out.pow(exp) + out=out/out.sum(1).view(-1,1).expand_as(out) + tar=tar.pow(exp) + tar=tar/tar.sum(1).view(-1,1).expand_as(tar) + out=out+eps/out.size(1) + out=out/out.sum(1).view(-1,1).expand_as(out) + ce=-(tar*out.log()).sum(1) + if size_average: + ce=ce.mean() + return ce + +######################################################################################################################## + + +def set_req_grad(layer, req_grad): + if hasattr(layer, 'weight'): + layer.weight.requires_grad=req_grad + if hasattr(layer, 'bias'): + layer.bias.requires_grad=req_grad + return + +######################################################################################################################## + + +def is_number(s): + try: + float(s) + return True + except ValueError: + pass + + try: + import unicodedata + unicodedata.numeric(s) + return True + except (TypeError, ValueError): + pass + + return False +######################################################################################################################## diff --git a/rag_depth/src/utilstool/experiment.py b/rag_depth/src/utilstool/experiment.py new file mode 100644 index 0000000..ade5fbf --- /dev/null +++ b/rag_depth/src/utilstool/experiment.py @@ -0,0 +1,161 @@ +from __future__ import print_function, division +import torch +import torch.nn as nn +import torch.nn.parallel +import torch.utils.data +from torch.autograd import Variable +import torchvision.utils as vutils +import torch.nn.functional as F +import numpy as np +import copy + + +def make_iterative_func(func): + def wrapper(vars): + if isinstance(vars, list): + return [wrapper(x) for x in vars] + elif isinstance(vars, tuple): + return tuple([wrapper(x) for x in vars]) + elif isinstance(vars, dict): + return {k: wrapper(v) for k, v in vars.items()} + else: + return func(vars) + + return wrapper + + +def make_nograd_func(func): + def wrapper(*f_args, **f_kwargs): + with torch.no_grad(): + ret = func(*f_args, **f_kwargs) + return ret + + return wrapper + + +@make_iterative_func +def tensor2float(vars): + if isinstance(vars, float): + return vars + elif isinstance(vars, torch.Tensor): + return vars.data.item() + else: + raise NotImplementedError("invalid input type for tensor2float") + + +@make_iterative_func +def tensor2numpy(vars): + if isinstance(vars, np.ndarray): + return vars + elif isinstance(vars, torch.Tensor): + return vars.data.cpu().numpy() + else: + raise NotImplementedError("invalid input type for tensor2numpy") + + +@make_iterative_func +def check_allfloat(vars): + assert isinstance(vars, float) + + +def save_scalars(logger, mode_tag, scalar_dict, global_step): + scalar_dict = tensor2float(scalar_dict) + for tag, values in scalar_dict.items(): + if not isinstance(values, list) and not isinstance(values, tuple): + values = [values] + for idx, value in enumerate(values): + scalar_name = '{}/{}'.format(mode_tag, tag) + # if len(values) > 1: + scalar_name = scalar_name + "_" + str(idx) + logger.add_scalar(scalar_name, value, global_step) + + +def save_images(logger, mode_tag, images_dict, global_step): + images_dict = tensor2numpy(images_dict) + for tag, values in images_dict.items(): + if not isinstance(values, list) and not isinstance(values, tuple): + values = [values] + for idx, value in enumerate(values): + if len(value.shape) == 3: + value = value[:, np.newaxis, :, :] + value = value[:1] + value = torch.from_numpy(value) + + image_name = '{}/{}'.format(mode_tag, tag) + if len(values) > 1: + image_name = image_name + "_" + str(idx) + logger.add_image(image_name, vutils.make_grid(value, padding=0, nrow=1, normalize=True, scale_each=True), + global_step) + + +def adjust_learning_rate(optimizer, epoch, base_lr, lrepochs): + splits = lrepochs.split(':') + assert len(splits) == 2 + + # parse the epochs to downscale the learning rate (before :) + downscale_epochs = [int(eid_str) for eid_str in splits[0].split(',')] + # parse downscale rate (after :) + downscale_rate = float(splits[1]) + print("downscale epochs: {}, downscale rate: {}".format(downscale_epochs, downscale_rate)) + + lr = base_lr + for eid in downscale_epochs: + if epoch >= eid: + lr /= downscale_rate + else: + break + print("setting learning rate to {}".format(lr)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + +class AverageMeter(object): + def __init__(self): + self.sum_value = 0. + self.count = 0 + + def update(self, x): + check_allfloat(x) + self.sum_value += x + self.count += 1 + + def mean(self): + return self.sum_value / self.count + + +class AverageMeterDict(object): + def __init__(self): + self.data = None + self.count = 0 + + def update(self, x): + check_allfloat(x) + self.count += 1 + if self.data is None: + self.data = copy.deepcopy(x) + else: + for k1, v1 in x.items(): + if isinstance(v1, float): + self.data[k1] += v1 + elif isinstance(v1, tuple) or isinstance(v1, list): + for idx, v2 in enumerate(v1): + self.data[k1][idx] += v2 + else: + assert NotImplementedError("error input type for update AvgMeterDict") + + def mean(self): + @make_iterative_func + def get_mean(v): + return v / float(self.count) + + return get_mean(self.data) + + +class silog_loss(nn.Module): + def __init__(self): + super(silog_loss, self).__init__() + self.variance_focus = 0.85 + + def forward(self, depth_est, depth_gt, mask): + d = torch.log(depth_est[mask]) - torch.log(depth_gt[mask]) + return torch.sqrt((d ** 2).mean() - self.variance_focus * (d.mean() ** 2)) * 10.0 diff --git a/rag_depth/src/utilstool/metrics.py b/rag_depth/src/utilstool/metrics.py new file mode 100644 index 0000000..4c02df2 --- /dev/null +++ b/rag_depth/src/utilstool/metrics.py @@ -0,0 +1,65 @@ +import torch +import torch.nn.functional as F +from utilstool.experiment import make_nograd_func +from torch.autograd import Variable +from torch import Tensor + + +# Update D1 from >3px to >=3px & >5% +# matlab code: +# E = abs(D_gt - D_est); +# n_err = length(find(D_gt > 0 & E > tau(1) & E. / abs(D_gt) > tau(2))); +# n_total = length(find(D_gt > 0)); +# d_err = n_err / n_total; + +def check_shape_for_metric_computation(*vars): + assert isinstance(vars, tuple) + for var in vars: + assert len(var.size()) == 3 + assert var.size() == vars[0].size() + +# a wrapper to compute metrics for each image individually +def compute_metric_for_each_image(metric_func): + def wrapper(D_ests, D_gts, masks, *nargs): + check_shape_for_metric_computation(D_ests, D_gts, masks) + bn = D_gts.shape[0] # batch size + results = [] # a list to store results for each image + # compute result one by one + for idx in range(bn): + # if tensor, then pick idx, else pass the same value + cur_nargs = [x[idx] if isinstance(x, (Tensor, Variable)) else x for x in nargs] + if masks[idx].float().mean() / (D_gts[idx] > 0).float().mean() < 0.1: + print("masks[idx].float().mean() too small, skip") + else: + ret = metric_func(D_ests[idx], D_gts[idx], masks[idx], *cur_nargs) + results.append(ret) + if len(results) == 0: + print("masks[idx].float().mean() too small for all images in this batch, return 0") + return torch.tensor(0, dtype=torch.float32, device=D_gts.device) + else: + return torch.stack(results).mean() + return wrapper + +@make_nograd_func +@compute_metric_for_each_image +def D1_metric(D_est, D_gt, mask): + D_est, D_gt = D_est[mask], D_gt[mask] + E = torch.abs(D_gt - D_est) + err_mask = (E > 3) & (E / D_gt.abs() > 0.05) + return torch.mean(err_mask.float()) + +@make_nograd_func +@compute_metric_for_each_image +def Thres_metric(D_est, D_gt, mask, thres): + assert isinstance(thres, (int, float)) + D_est, D_gt = D_est[mask], D_gt[mask] + E = torch.abs(D_gt - D_est) + err_mask = E > thres + return torch.mean(err_mask.float()) + +# NOTE: please do not use this to build up training loss +@make_nograd_func +@compute_metric_for_each_image +def EPE_metric(D_est, D_gt, mask): + D_est, D_gt = D_est[mask], D_gt[mask] + return F.l1_loss(D_est, D_gt, size_average=True)