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How to use test_forward_dir method in autotest

Best Python code snippet using autotest_python

utils_unittest.py

Source:utils_unittest.py

...363    def test_not_absolute(self):364        self.assertRaises(AssertionError, utils.get_relative_path, "a", "b")365    def test_same_dir(self):366        self.assertEqual(utils.get_relative_path("/a/b/c", "/a/b"), "c")367    def test_forward_dir(self):368        self.assertEqual(utils.get_relative_path("/a/b/c/d", "/a/b"), "c/d")369    def test_previous_dir(self):370        self.assertEqual(utils.get_relative_path("/a/b", "/a/b/c/d"), "../..")371    def test_parallel_dir(self):372        self.assertEqual(utils.get_relative_path("/a/c/d", "/a/b/c/d"),373                         "../../../c/d")374if __name__ == "__main__":...

test_spring_opt.py

Source:test_spring_opt.py

...168                # cr_path=join(self.cr_dir,'cr_{:08d}.obj'.format(sample_id))169                # self.write_obj(x_save,None,cr_path,patch_id=self.patch_id)170            except:171                continue172    def test_forward_dir(self,in_dir,out_dir,n_iters=1,start=0,end=2247):173        system=SpringOptSystem(self.stiffen_anchors_net,self.stiffen_anchors_reg,self.edges,self.l0,self.k,m_alpha=0.1,axial_data=self.axial_data)174        opt=NewtonOpt(system,newton_tol=1e-12,cg_tol=1e-3,cg_max_iter=250)175        if not isdir(out_dir):176            os.makedirs(out_dir)177        for sample_id in range(start,end+1):178            pd_path=join(in_dir,'{:08d}.obj'.format(sample_id))179            v,f=self.read_obj(pd_path,patch_id=self.patch_id)180            v=torch.from_numpy(v).to(device=self.device,dtype=self.dtype)181            x=v182            start_time=time.time()183            for i in range(n_iters):184                x,data,success=opt.solve(v,x)185                end_time=time.time()186                print('forward time:',end_time-start_time)187            x_save=x.detach().cpu().numpy()188            cr_path=join(out_dir,'{:08d}.obj'.format(sample_id))189            self.write_obj(x_save,f,cr_path,patch_id=self.patch_id)190    def test_backward(self,sample_id):191        pd_path=join(self.pd_dir,'pd_{:08d}.obj'.format(sample_id))192        pd_v,f=self.read_obj(pd_path,patch_id=self.patch_id)193        pd_v=torch.from_numpy(pd_v).to(device=self.device,dtype=self.dtype)194        gt_path=join(self.pd_dir,'gt_{:08d}.obj'.format(sample_id))195        gt_v,_=self.read_obj(gt_path,patch_id=self.patch_id)196        gt_v=torch.from_numpy(gt_v).to(device=self.device,dtype=self.dtype)197        dv=pd_v-gt_v198        cr_path=join(self.cr_dir,'cr_{:08d}.npy'.format(sample_id))199        cr_v=np.load(cr_path)200        cr_v=torch.from_numpy(cr_v).to(device=self.device,dtype=self.dtype)201        # m_path=join(self.cr_dir,'m_{:08d}.npy'.format(sample_id))202        # m_adjusted=np.load(m_path)203        # m_adjusted=torch.from_numpy(m_adjusted).to(device=self.device,dtype=self.dtype)204        # system=SpringOptSystem(self.m,self.edges,self.l0,self.k,m_alpha=0.1,axial_data=self.axial_data)205        system=SpringOptSystem(self.stiffen_anchors_net,self.stiffen_anchors_reg,self.edges,self.l0,self.k,m_alpha=0.1,axial_data=self.axial_data)206        system.use_m_adjusted=False207        data=system.get_data(cr_v)208        data['c']=pd_v209        data['anchors_net']=pd_v210        data['anchors_reg']=pd_v211        data['stiffen_anchors_net']=self.stiffen_anchors_net212        data['stiffen_anchors_reg']=self.stiffen_anchors_reg213        # data['m_adjusted']=m_adjusted214        J=system.get_J(data)215        norm_J=torch.norm(J)216        data['J_rms']=norm_J/np.sqrt(len(cr_v))217        dx=spring_opt_backward(system,data,dv,cg_tol=1e-3,cg_max_iter=250)218        grad_path=join(self.cr_dir,'grad_{:08d}.npy'.format(sample_id))219        print('save to',grad_path)220        np.save(grad_path,dx.cpu().numpy())221    def test_grad(self,sample_id):222        pd_path=join(self.pd_dir,'pd_{:08d}.obj'.format(sample_id))223        print('pd_path',pd_path)224        pd_vt,_=self.read_obj(pd_path,patch_id=self.patch_id)225        grad=np.load(join(self.cr_dir,'grad_{:08d}.npy'.format(sample_id)))226        print('grad.norm',np.linalg.norm(grad))227        grad_len=1228        ed_vt=pd_vt-grad*grad_len229        n_vts=len(pd_vt)230        obj_path=join(self.cr_dir,'grad_{:08d}.obj'.format(sample_id))231        print('write to',obj_path)232        with open(obj_path,'w') as f:233            for v in pd_vt:234                f.write('v {} {} {}\n'.format(v[0],v[1],v[2]))235            for v in ed_vt:236                f.write('v {} {} {}\n'.format(v[0],v[1],v[2]))237            for i in range(n_vts):238                f.write('l {} {}\n'.format(i+1,i+1+n_vts))239    def test_module(self,sample_id,n_iters=1):240        pd_path=join(self.pd_dir,'pd_{:08d}.obj'.format(sample_id))241        pd_v,f=self.read_obj(pd_path,patch_id=self.patch_id)242        pd_v=torch.from_numpy(pd_v).to(device=self.device,dtype=self.dtype).unsqueeze(0)243        gt_path=join(self.pd_dir,'gt_{:08d}.obj'.format(sample_id))244        gt_v,_=self.read_obj(gt_path,patch_id=self.patch_id)245        gt_v=torch.from_numpy(gt_v).to(device=self.device,dtype=self.dtype).unsqueeze(0)246        proj_module=SpringOptModule(self.res_ctx,self.ctx)247        x=pd_v248        x.requires_grad_(True)249        for i in range(n_iters):250            x=proj_module(x)251        loss=torch.sum((gt_v-x)**2)/2252        loss.backward()253        print('grad.norm',torch.norm(pd_v.grad))254    def test_loss_along_line(self,sample_id,n_iters):255        pd_path=join(self.pd_dir,'pd_{:08d}.obj'.format(sample_id))256        pd_v,f=self.read_obj(pd_path,patch_id=self.patch_id)257        pd_v=torch.from_numpy(pd_v).to(device=self.device,dtype=self.dtype).unsqueeze(0)258        # gt_vt=np.load(join(self.data_root_dir,'lowres_skin_npys/skin_{:08d}.npy'.format(sample_id)))+np.load(join(self.data_root_dir,'lowres_offsets_i10/offset_{:08d}.npy'.format(sample_id)))259        gt_path=join(self.pd_dir,'gt_{:08d}.obj'.format(sample_id))260        gt_v,f=self.read_obj(gt_path,patch_id=self.patch_id)261        gt_v=torch.from_numpy(gt_v).to(device=self.device,dtype=self.dtype).unsqueeze(0)262        proj_module=SpringOptModule(self.res_ctx,self.ctx)263        def f(x):264            for i in range(n_iters):265                x=proj_module(x)266            return torch.sum(((x-gt_v)**2).view(x.size(0),-1),dim=1)/2267        pd_v.requires_grad_(True)268        loss=f(pd_v)269        loss.backward()270        g=pd_v.grad[0]271        pd_v.requires_grad_(False)272        loss_list=[]273        total_n=100274        processed_n=0275        end=2276        batch_size=1277        while processed_n<total_n:278            x=pd_v.repeat(batch_size,1,1)279            for i in range(batch_size):280                t=(i+processed_n)/total_n*end281                x[i]-=t*g282            loss=f(x)283            loss_list+=loss.tolist()284            processed_n+=batch_size285        print(loss_list)286        np.savetxt(join(self.opt_dir,'loss_{}.txt'.format(end)),np.array(loss_list))287    def plot_loss_along_line(self,sample_id):288        end=2289        loss=np.loadtxt(join(self.opt_dir,'loss_{}.txt'.format(2)))290        x=np.linspace(0,end,len(loss))291        fig=plt.gcf()292        ax=plt.gca()293        ax.plot(x,loss)294        ax.set_title('iter=10')295        plot_path=join(self.opt_dir,'loss_{}.png'.format(end))296        print('plot_path',plot_path)297        fig.savefig(plot_path)298if __name__=='__main__':299    parser=argparse.ArgumentParser()300    parser.add_argument('-start',type=int,default=0)301    parser.add_argument('-end',type=int,default=0)302    args=parser.parse_args()303    test=SpringOptTest()304    # test.test_forward(106,n_iters=1)305    # test.test_dataset(args.start,args.end)306    test.test_backward(106)307    test.test_grad(106)308    # test.test_module(106,n_iters=10)309    # test.test_loss_along_line(106,n_iters=10)310    # test.plot_loss_along_line(106)311    # test.test_forward_dir('/data/zhenglin/PhysBAM/Private_Projects/cloth_on_virtual_body/joint_data/seq1/videos/collected_objs','/data/zhenglin/PhysBAM/Private_Projects/cloth_on_virtual_body/joint_data/seq1/videos/corrected_objs',start=args.start,end=args.end)...

imgpreprocess.py

Source:imgpreprocess.py

1import numpy as np2import matplotlib3import h5py4import os, shutil5import matplotlib.pyplot as plt6from keras.optimizers import SGD7from keras import layers, models, optimizers8from keras.preprocessing import image9from keras.preprocessing.image import ImageDataGenerator10from keras.callbacks import EarlyStopping, ModelCheckpoint11from model import SqueezeNet1213import tensorflow as tf1415from keras.datasets import mnist16from keras.utils import np_utils17from keras.models import Sequential18from keras.layers import Dense192021###############################################################################22# Diretory Path Creation23# Can separate this code to different python file and just import to this24#original_dataset_dir = '/Users/jisuk/OneDrive/ë°í íë©´/datasets/catsAndDogs/train'25base_dir = '/home/jisukim/eye1001/datasets/eyesmall6'2627if os.path.exists(base_dir):  # ë°ë³µì ì¸ ì¤íì ìí´ ëë í ë¦¬ë¥¼ ìì í©ëë¤.28    shutil.rmtree(base_dir)   # ì´ ì½ëë ì±ì í¬í¨ëì´ ìì§ ììµëë¤.29os.mkdir(base_dir)30# íë ¨, ê²ì¦, íì¤í¸ ë¶í ì ìí ëë í°ë¦¬31train_dir = os.path.join(base_dir, 'train')32# os.path.join = base_dirì ì ì¸ë ì£¼ìì  'train' ì´ë¼ë í´ëë¥¼ ìì± 33# ./datasets/cats_and_dogs_small/train 3435os.mkdir(train_dir)36# train_dirì´ë¼ë ê²½ë¡ë¥¼ ì¤ì ë¡ make directoryí¨ 3738validation_dir = os.path.join(base_dir, 'validation')39os.mkdir(validation_dir)4041test_dir = os.path.join(base_dir, 'test')42os.mkdir(test_dir)4344# ì´ í¸ë ì¸, ê²ì¦, íì¤í¸ë¼ë í´ëë¥¼ ìì±4546# íë ¨ì©47train_forward_dir = os.path.join(train_dir, 'forward')48os.mkdir(train_forward_dir)4950train_closed_dir = os.path.join(train_dir, 'side')51os.mkdir(train_closed_dir)525354# ê²ì¦ì©55validation_forward_dir = os.path.join(validation_dir, 'forward')56os.mkdir(validation_forward_dir)5758validation_closed_dir = os.path.join(validation_dir, 'side')59os.mkdir(validation_closed_dir)606162# íì¤í¸ì©63test_forward_dir = os.path.join(test_dir, 'forward')64os.mkdir(test_forward_dir)6566test_closed_dir = os.path.join(test_dir, 'side')67os.mkdir(test_closed_dir)6869#################################side#####################################	70	71fnames = ['{}.JPG'.format(i) for i in range(501,1001)]72for fname in fnames:73    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/train/side", fname)74    dst = os.path.join(train_closed_dir, fname)75    shutil.copyfile(src, dst)	76	77	78fnames = ['{}.JPG'.format(i) for i in range(201,501)]79for fname in fnames:80    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/validation/side", fname)81    dst = os.path.join(validation_closed_dir, fname)82    shutil.copyfile(src, dst)8384fnames = ['{}.JPG'.format(i) for i in range(1,201)]85for fname in fnames:86    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/test/side", fname)87    dst = os.path.join(test_closed_dir, fname)88    shutil.copyfile(src, dst)8990###############################forward#######################################9192fnames = ['{}.JPG'.format(i) for i in range(501,1001)]93for fname in fnames:94    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/train/forward", fname)95    dst = os.path.join(train_forward_dir, fname)96    shutil.copyfile(src, dst)97	98fnames = ['{}.JPG'.format(i) for i in range(201,501)]99for fname in fnames:100    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/validation/forward", fname)101    dst = os.path.join(validation_forward_dir, fname)102    shutil.copyfile(src, dst)103    104fnames = ['{}.JPG'.format(i) for i in range(1,201)]105for fname in fnames:106    src = os.path.join("/home/jisukim/eye1001/datasets/eye6/eye/test/forward", fname)107    dst = os.path.join(test_forward_dir, fname)108    shutil.copyfile(src, dst)109	110########################################################################################111# Start learning, as well as compiling model112sn = SqueezeNet(input_shape = (100, 75, 3), nb_classes=2)113'''114sn = Sequential()115sn.add(Flatten(input_shape=train_data.shape[1:]))116sn.add(Dense(256, activation='relu',kernel_regularizer=keras.regularizers.l2(0.001)))117sn.add(Dropout(0.3))118sn.add(BatchNormalization())119sn.add(Dense(2, activation='softmax'))120'''121sn.summary()122train_data_dir = '/home/jisukim/eye1001/datasets/eyesmall6/train'123validation_data_dir = '/home/jisukim/eye1001/datasets/eyesmall6/validation'124test_data_dir  = '/home/jisukim/eye1001/datasets/eyesmall6/test'125train_samples = 1000126validation_samples = 600127epochs = 50128nb_class = 2129width, height = 100, 75130131sgd = SGD(lr=0.001, decay=0.0002, momentum=0.9, nesterov=True)132sn.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy', tf.keras.metrics.Precision(name='precision'), tf.keras.metrics.Recall(name='recall'), tf.keras.metrics.FalsePositives(name='false_positives'),tf.keras.metrics.FalseNegatives(name='false_negatives')])133134#   Generator135train_datagen = ImageDataGenerator(136        rescale=1./255,137        shear_range=0.2,138        zoom_range=0.2,139        horizontal_flip=True)140141test_datagen = ImageDataGenerator(rescale=1./255)142143train_generator = train_datagen.flow_from_directory(144        train_data_dir,145        target_size=(width, height),146        batch_size=32,147        class_mode='categorical')148149validation_generator = test_datagen.flow_from_directory(150        validation_data_dir,151        target_size=(width, height),152        batch_size=32,153        class_mode='categorical')154		155test_generator = test_datagen.flow_from_directory(156        test_data_dir,157        target_size=(width, height),  158        batch_size=32,159        class_mode='categorical')160############################################################################161# Inlcude this Callback checkpoint if you want to make .h5 checkpoint files162# May slow your training163#early_stopping = EarlyStopping(monitor='val_loss', patience=3, verbose=0)164#checkpoint = ModelCheckpoint(                                         165#                'weights.{epoch:02d}-{val_loss:.2f}.h5',166#                monitor='val_loss',                               167#                verbose=0,                                        168#                save_best_only=True,                              169#                save_weights_only=True,                           170#                mode='min',                                       171#                period=1)                                172###########################################################################173174hist=sn.fit_generator(175        train_generator,176        steps_per_epoch=train_samples,177        epochs=epochs,178        validation_data=validation_generator,179        validation_steps=validation_samples 180        #,callbacks=[checkpoint]181)182183#########################################################################################33184185fig, loss_ax = plt.subplots()186187acc_ax = loss_ax.twinx()188189loss_ax.plot(hist.history['loss'], 'y', label='train loss')190loss_ax.plot(hist.history['val_loss'], 'g', label='val loss')191192acc_ax.plot(hist.history['accuracy'], 'r', label='train acc')193acc_ax.plot(hist.history['val_accuracy'], 'b', label='val acc')194195loss_ax.set_xlabel('epoch')196loss_ax.set_ylabel('loss')197acc_ax.set_ylabel('accuray')198199loss_ax.legend(loc='lower left')200acc_ax.legend(loc='upper left')201202plt.savefig('ourmodel2.png')203204###########################################################################205206print("-- Evaluate --")207scores = sn.evaluate_generator(test_generator, steps=5)208print("%s: %.2f%%" %(sn.metrics_names[1], scores[1]*100))209210print("-- Predict --")211output = sn.predict_generator(test_generator, steps=5)212np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})213214print(test_generator.class_indices)215print(output)216217print("Training Ended")218219sn.save_weights('ourweights2.h5')220print("Saved weight file")221222sn.save('ourmodel2.h5')223print("saved model file")224225# End of Code
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