Merge branch 'develop' of https://github.com/PaddlePaddle/models into youtube_05130940

PaddleCV/3d_vision/PointNet++/models/pointnet2_modules.py

@@ -200,8 +200,7 @@ def pointnet_fp_module(unknown, known, unknown_feats, known_feats, mlp, bn=True,
         dist.stop_gradient = True
         idx.stop_gradient = True
         dist = fluid.layers.sqrt(dist)
-        ones = fluid.layers.fill_constant_batch_size_like(dist, dist.shape, dist.dtype, 1)
-        dist_recip = ones / (dist + 1e-8); # 1.0 / dist
+        dist_recip = 1.0 / (dist + 1e-8); # 1.0 / dist
         norm = fluid.layers.reduce_sum(dist_recip, dim=-1, keep_dim=True)
         weight = dist_recip / norm
         weight.stop_gradient = True

PaddleCV/3d_vision/PointRCNN/models/loss_utils.py

@@ -93,8 +93,8 @@ def get_reg_loss(pred_reg, reg_label, fg_mask, point_num, loc_scope,
         x_res_norm_label = x_res_label / loc_bin_size
         z_res_norm_label = z_res_label / loc_bin_size
 
-        x_bin_onehot = fluid.layers.one_hot(x_bin_label, depth=per_loc_bin_num)
-        z_bin_onehot = fluid.layers.one_hot(z_bin_label, depth=per_loc_bin_num)
+        x_bin_onehot = fluid.one_hot(x_bin_label[:, 0], depth=per_loc_bin_num)
+        z_bin_onehot = fluid.one_hot(z_bin_label[:, 0], depth=per_loc_bin_num)
 
         loss_x_res = fluid.layers.smooth_l1(fluid.layers.reduce_sum(pred_reg[:, x_res_l: x_res_r] * x_bin_onehot, dim=1, keep_dim=True), x_res_norm_label)
         loss_x_res = fluid.layers.reduce_mean(loss_x_res * fg_mask) * fg_scale
@@ -115,7 +115,7 @@ def get_reg_loss(pred_reg, reg_label, fg_mask, point_num, loc_scope,
         y_res_label = y_shift - (fluid.layers.cast(y_bin_label, dtype=y_shift.dtype) * loc_y_bin_size + loc_y_bin_size / 2.)
         y_res_norm_label = y_res_label / loc_y_bin_size
 
-        y_bin_onehot = fluid.layers.one_hot(y_bin_label, depth=per_loc_bin_num)
+        y_bin_onehot = fluid.one_hot(y_bin_label[:, 0], depth=per_loc_bin_num)
 
         loss_y_bin = fluid.layers.cross_entropy(pred_reg[:, y_bin_l: y_bin_r], y_bin_label)
         loss_y_bin = fluid.layers.reduce_mean(loss_y_bin * fg_mask) * fg_scale
@@ -169,7 +169,7 @@ def get_reg_loss(pred_reg, reg_label, fg_mask, point_num, loc_scope,
         ry_res_label = shift_angle - (fluid.layers.cast(ry_bin_label, dtype=shift_angle.dtype) * angle_per_class + angle_per_class / 2)
         ry_res_norm_label = ry_res_label / (angle_per_class / 2)
 
-    ry_bin_onehot = fluid.layers.one_hot(ry_bin_label, depth=num_head_bin)
+    ry_bin_onehot = fluid.one_hot(ry_bin_label[:, 0], depth=num_head_bin)
     loss_ry_bin = fluid.layers.softmax_with_cross_entropy(pred_reg[:, ry_bin_l:ry_bin_r], ry_bin_label)
     loss_ry_bin = fluid.layers.reduce_mean(loss_ry_bin * fg_mask) * fg_scale
     loss_ry_res = fluid.layers.smooth_l1(fluid.layers.reduce_sum(pred_reg[:, ry_res_l: ry_res_r] * ry_bin_onehot, dim=1, keep_dim=True), ry_res_norm_label)

PaddleCV/3d_vision/PointRCNN/models/pointnet2_modules.py

@@ -184,8 +184,7 @@ def pointnet_fp_module(unknown, known, unknown_feats, known_feats, mlp, bn=True,
         dist.stop_gradient = True
         idx.stop_gradient = True
         dist = fluid.layers.sqrt(dist)
-        ones = fluid.layers.fill_constant_batch_size_like(dist, dist.shape, dist.dtype, 1)
-        dist_recip = ones / (dist + 1e-8); # 1.0 / dist
+        dist_recip = 1.0 / (dist + 1e-8); # 1.0 / dist
         norm = fluid.layers.reduce_sum(dist_recip, dim=-1, keep_dim=True)
         weight = dist_recip / norm
         weight.stop_gradient = True

PaddleCV/3d_vision/PointRCNN/utils/optimizer.py

@@ -54,21 +54,31 @@ def cosine_warmup_decay(learning_rate, betas, warmup_factor, decay_factor,
     warmup_step_var = fluid.layers.fill_constant(
         shape=[1], dtype='float32', value=float(warmup_step), force_cpu=True)
 
-    with control_flow.Switch() as switch:
-        with switch.case(global_step < warmup_step_var):
-            cur_lr = annealing_cos(warmup_start_lr, learning_rate,
-                                   global_step / warmup_step_var)
-            fluid.layers.assign(cur_lr, lr)
-            cur_beta1 = annealing_cos(betas[0], betas[1],
-                                   global_step / warmup_step_var)
-            fluid.layers.assign(cur_beta1, beta1)
-        with switch.case(global_step >= warmup_step_var):
-            cur_lr = annealing_cos(learning_rate, decay_end_lr,
-                                   (global_step - warmup_step_var) / (total_step - warmup_step))
-            fluid.layers.assign(cur_lr, lr)
-            cur_beta1 = annealing_cos(betas[1], betas[0],
-                                   (global_step - warmup_step_var) / (total_step - warmup_step))
-            fluid.layers.assign(cur_beta1, beta1)
+    warmup_pred = global_step < warmup_step_var
+    decay_pred = global_step >= warmup_step_var
+
+    # learning rate warmup and decay
+    def warmup_lr():
+        return annealing_cos(warmup_start_lr, learning_rate,
+                             global_step / warmup_step_var)
+    def decay_lr():
+        return annealing_cos(learning_rate, decay_end_lr,
+                (global_step - warmup_step_var) / (total_step - warmup_step))
+
+    lr = fluid.layers.case(pred_fn_pairs=[(warmup_pred, warmup_lr),
+                                          (decay_pred, decay_lr)])
+
+    # Adam beta1 warmup and decay
+    def warmup_beta1():
+        return annealing_cos(betas[0], betas[1],
+                             global_step / warmup_step_var)
+
+    def decay_beta1():
+        return annealing_cos(betas[0], betas[1],
+                             global_step / warmup_step_var)
+
+    beta1 = fluid.layers.case(pred_fn_pairs=[(warmup_pred, warmup_beta1),
+                                          (decay_pred, decay_beta1)])
 
     return lr, beta1
 
@@ -96,11 +106,11 @@ def optimize(loss,
         raise ValueError("Unkown learning rate scheduler, should be "
                          "'cosine_warmup_decay'")
 
+    grad_clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm)
     optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr,
                                      beta1=scheduled_beta1,
-                                     beta2=beta2)
-    fluid.clip.set_gradient_clip(
-        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm))
+                                     beta2=beta2,
+                                     grad_clip=grad_clip)
 
     param_list = dict()
 

PaddleCV/gan/c_gan/c_gan.py

@@ -79,10 +79,11 @@ def train(args):
         g_program_test = dg_program.clone(for_test=True)
 
         dg_logit = D_cond(g_img, conditions)
+        dg_logit_shape = fluid.layers.shape(dg_logit)
         dg_loss = loss(
             dg_logit,
-            fluid.layers.fill_constant_batch_size_like(
-                input=noise, dtype='float32', shape=[-1, 1], value=1.0))
+            fluid.layers.fill_constant(
+                dtype='float32', shape=[dg_logit_shape[0], 1], value=1.0))
 
     opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)
 
@@ -96,11 +97,11 @@ def train(args):
         exe = fluid.Executor(fluid.CUDAPlace(0))
     exe.run(fluid.default_startup_program())
     if args.run_ce:
-        train_reader = paddle.batch(
+        train_reader = fluid.io.batch(
             paddle.dataset.mnist.train(), batch_size=args.batch_size)
     else:
-        train_reader = paddle.batch(
-            paddle.reader.shuffle(
+        train_reader = fluid.io.batch(
+            fluid.io.shuffle(
                 paddle.dataset.mnist.train(), buf_size=60000),
             batch_size=args.batch_size)
 

PaddleCV/gan/c_gan/dc_gan.py

@@ -74,10 +74,11 @@ def train(args):
         g_program_test = dg_program.clone(for_test=True)
 
         dg_logit = D(g_img)
+        dg_logit_shape = fluid.layers.shape(dg_logit)
         dg_loss = loss(
             dg_logit,
-            fluid.layers.fill_constant_batch_size_like(
-                input=noise, dtype='float32', shape=[-1, 1], value=1.0))
+            fluid.layers.fill_constant(
+                shape=[dg_logit_shape[0], 1], dtype='float32', value=1.0))
 
     opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)
 
@@ -92,11 +93,11 @@ def train(args):
     exe.run(fluid.default_startup_program())
 
     if args.run_ce:
-        train_reader = paddle.batch(
+        train_reader = fluid.io.batch(
             paddle.dataset.mnist.train(), batch_size=args.batch_size)
     else:
-        train_reader = paddle.batch(
-            paddle.reader.shuffle(
+        train_reader = fluid.io.batch(
+            fluid.io.shuffle(
                 paddle.dataset.mnist.train(), buf_size=60000),
             batch_size=args.batch_size)
 

PaddleCV/gan/c_gan/network.py

@@ -34,6 +34,7 @@ use_cudnn = True
 if 'ce_mode' in os.environ:
     use_cudnn = False
 
+
 def bn(x, name=None, act='relu'):
     if name is None:
         name = get_parent_function_name()
@@ -100,8 +101,11 @@ def deconv(x,
 
 def conv_cond_concat(x, y):
     """Concatenate conditioning vector on feature map axis."""
-    ones = fluid.layers.fill_constant_batch_size_like(
-        x, [-1, y.shape[1], x.shape[2], x.shape[3]], "float32", 1.0)
+    x_shape = fluid.layers.shape(x)
+    ones = fluid.layers.fill_constant(
+        shape=[x_shape[0], y.shape[1], x.shape[2], x.shape[3]],
+        dtype='float32',
+        value=1.0)
     return fluid.layers.concat([x, ones * y], 1)
 
 

PaddleCV/gan/data_reader.py

@@ -22,6 +22,7 @@ import argparse
 import struct
 import os
 import paddle
+import paddle.fluid as fluid
 import random
 import sys
 
@@ -99,16 +100,24 @@ class reader_creator(object):
 
     def make_reader(self, args, return_name=False):
         print(self.image_dir, self.list_filename)
+        self.with_label = False
 
         def reader():
             batch_out = []
+            batch_out_label = []
             batch_out_name = []
 
             if self.shuffle:
                 np.random.shuffle(self.lines)
 
-            for i, file in enumerate(self.lines):
-                file = file.strip('\n\r\t ')
+            for i, line in enumerate(self.lines):
+                line = line.strip('\n\r\t').split(' ')
+                if len(line) > 1:
+                    self.with_label = True
+                    batch_out_label.append(line[1])
+                    file = line[0]
+                else:
+                    file = line[0]
                 self.name2id[os.path.basename(file)] = i
                 self.id2name[i] = os.path.basename(file)
                 img = Image.open(os.path.join(self.image_dir, file)).convert(
@@ -133,10 +142,18 @@ class reader_creator(object):
                     batch_out.append(img)
                 if len(batch_out) == self.batch_size:
                     if return_name:
-                        yield batch_out, batch_out_name
+                        if self.with_label:
+                            yield [[batch_out, batch_out_label, batch_out_name]]
+                            batch_out_label = []
+                        else:
+                            yield batch_out, batch_out_name
                         batch_out_name = []
                     else:
-                        yield [batch_out]
+                        if self.with_label:
+                            yield [[batch_out, batch_out_label]]
+                            batch_out_label = []
+                        else:
+                            yield [batch_out]
                     batch_out = []
 
         return reader
@@ -504,8 +521,8 @@ class data_reader(object):
             train_labels = os.path.join(self.cfg.data_dir, self.cfg.dataset,
                                         "train-labels-idx1-ubyte.gz")
 
-            train_reader = paddle.batch(
-                paddle.reader.shuffle(
+            train_reader = fluid.io.batch(
+                fluid.io.shuffle(
                     mnist_reader_creator(train_images, train_labels, 100),
                     buf_size=60000),
                 batch_size=self.cfg.batch_size)
@@ -667,8 +684,9 @@ class data_reader(object):
                         image_dir=dataset_dir,
                         list_filename=test_list,
                         batch_size=self.cfg.n_samples)
-                    reader_test = test_reader.get_test_reader(
+                    reader_test = test_reader.make_reader(
                         self.cfg, shuffle=False, return_name=True)
                     id2name = test_reader.id2name
                 batch_num = train_reader.len()
-                return train_reader, reader_test, batch_num, id2name
+                reader = train_reader.make_reader(self.cfg)
+                return reader, reader_test, batch_num, id2name

PaddleCV/gan/network/AttGAN_network.py

@@ -60,9 +60,14 @@ class AttGAN_model(object):
 
     def concat(self, z, a):
         """Concatenate attribute vector on feature map axis."""
-        ones = fluid.layers.fill_constant_batch_size_like(
-            z, [-1, a.shape[1], z.shape[2], z.shape[3]], "float32", 1.0)
-        return fluid.layers.concat([z, fluid.layers.elementwise_mul(ones, a, axis=0)], axis=1)
+        batch = fluid.layers.shape(z)[0]
+        ones = fluid.layers.fill_constant(
+            shape=[batch, a.shape[1], z.shape[2], z.shape[3]],
+            dtype="float32",
+            value=1.0)
+        return fluid.layers.concat(
+            [z, fluid.layers.elementwise_mul(
+                ones, a, axis=0)], axis=1)
 
     def Genc(self, input, dim=64, n_layers=5, name='G_enc_', is_test=False):
         z = input

PaddleCV/gan/network/STGAN_network.py

@@ -82,9 +82,14 @@ class STGAN_model(object):
 
     def concat(self, z, a):
         """Concatenate attribute vector on feature map axis."""
-        ones = fluid.layers.fill_constant_batch_size_like(
-            z, [-1, a.shape[1], z.shape[2], z.shape[3]], "float32", 1.0)
-        return fluid.layers.concat([z, fluid.layers.elementwise_mul(ones, a, axis=0)], axis=1)
+        batch = fluid.layers.shape(z)[0]
+        ones = fluid.layers.fill_constant(
+            shape=[batch, a.shape[1], z.shape[2], z.shape[3]],
+            dtype="float32",
+            value=1.0)
+        return fluid.layers.concat(
+            [z, fluid.layers.elementwise_mul(
+                ones, a, axis=0)], axis=1)
 
     def Genc(self, input, dim=64, n_layers=5, name='G_enc_', is_test=False):
         z = input

PaddleCV/gan/network/base_network.py

@@ -42,7 +42,9 @@ def norm_layer(input,
     if norm_type == 'batch_norm':
         if affine == True:
             param_attr = fluid.ParamAttr(
-                name=name + '_w', initializer=fluid.initializer.Normal(loc=1.0, scale=0.02))
+                name=name + '_w',
+                initializer=fluid.initializer.Normal(
+                    loc=1.0, scale=0.02))
             bias_attr = fluid.ParamAttr(
                 name=name + '_b',
                 initializer=fluid.initializer.Constant(value=0.0))
@@ -366,8 +368,11 @@ def linear(input,
 
 
 def conv_cond_concat(x, y):
-    ones = fluid.layers.fill_constant_batch_size_like(
-        x, [-1, y.shape[1], x.shape[2], x.shape[3]], "float32", 1.0)
+    batch = fluid.layers.shape(x)[0]
+    ones = fluid.layers.fill_constant(
+        shape=[ones, y.shape[1], x.shape[2], x.shape[3]],
+        dtype="float32",
+        value=1.0)
     out = fluid.layers.concat([x, ones * y], 1)
     return out
 

PaddleCV/gan/trainer/AttGAN.py

@@ -44,11 +44,9 @@ class GTrainer():
                 self.g_loss_fake = -1 * fluid.layers.mean(self.pred_fake)
             #lsgan
             elif cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_fake,
-                    shape=self.pred_fake.shape,
-                    value=1.0,
-                    dtype='float32')
+                fake_shape = fluid.layers.shape(self.pred_fake)
+                ones = fluid.layers.fill_constant(
+                    shape=fake_shape, value=1.0, dtype='float32')
                 self.g_loss_fake = fluid.layers.mean(
                     fluid.layers.square(
                         fluid.layers.elementwise_sub(
@@ -106,11 +104,9 @@ class DTrainer():
                 self.d_loss = self.d_loss_real + self.d_loss_fake + 1.0 * self.d_loss_cls + cfg.lambda_gp * self.d_loss_gp
             #lsgan
             elif cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_real,
-                    shape=self.pred_real.shape,
-                    value=1.0,
-                    dtype='float32')
+                real_shape = fluid.layers.shape(self.pred_real)
+                ones = fluid.layers.fill_constant(
+                    shape=real_shape, value=1.0, dtype='float32')
                 self.d_loss_real = fluid.layers.mean(
                     fluid.layers.square(
                         fluid.layers.elementwise_sub(
@@ -145,31 +141,31 @@ class DTrainer():
 
     def gradient_penalty(self, f, real, fake=None, cfg=None, name=None):
         def _interpolate(a, b=None):
+            a_shape = fluid.layers.shape(a)
             if b is None:
                 if cfg.enable_ce:
-                    beta = fluid.layers.uniform_random_batch_size_like(
-                       input=a, shape=a.shape, min=0.0, max=1.0, seed=1)
+                    beta = fluid.layers.uniform_random(
+                        shape=a_shape, min=0.0, max=1.0, seed=1)
                 else:
-                    beta = fluid.layers.uniform_random_batch_size_like(
-                       input=a, shape=a.shape, min=0.0, max=1.0)
+                    beta = fluid.layers.uniform_random(
+                        shape=a_shape, min=0.0, max=1.0)
 
                 mean = fluid.layers.reduce_mean(
-                    a, dim=list(range(len(a.shape))), keep_dim=True)
+                    a, dim=list(range(len(a.shape))))
                 input_sub_mean = fluid.layers.elementwise_sub(a, mean, axis=0)
                 var = fluid.layers.reduce_mean(
                     fluid.layers.square(input_sub_mean),
-                    dim=list(range(len(a.shape))),
-                    keep_dim=True)
+                    dim=list(range(len(a.shape))))
                 b = beta * fluid.layers.sqrt(var) * 0.5 + a
             shape = [a.shape[0]]
             if cfg.enable_ce:
-               alpha = fluid.layers.uniform_random_batch_size_like(
-                 input=a, shape=shape, min=0.0, max=1.0, seed=1)
+                alpha = fluid.layers.uniform_random(
+                    shape=a_shape[0], min=0.0, max=1.0, seed=1)
             else:
-               alpha = fluid.layers.uniform_random_batch_size_like(
-                 input=a, shape=shape, min=0.0, max=1.0)
+                alpha = fluid.layers.uniform_random(
+                    shape=a_shape[0], min=0.0, max=1.0)
 
-            inner = fluid.layers.elementwise_mul((b-a), alpha, axis=0) + a
+            inner = fluid.layers.elementwise_mul((b - a), alpha, axis=0) + a
             return inner
 
         x = _interpolate(real, fake)
@@ -336,7 +332,7 @@ class AttGAN(object):
         if self.cfg.enable_ce:
             gen_trainer_program.random_seed = 90
             dis_trainer_program.random_seed = 90
-        
+
         t_time = 0
 
         for epoch_id in range(self.cfg.epoch):
@@ -379,7 +375,7 @@ class AttGAN(object):
                 sys.stdout.flush()
                 batch_id += 1
                 if self.cfg.enable_ce and batch_id == 100:
-                   break
+                    break
 
             if self.cfg.run_test:
                 image_name = fluid.data(
@@ -402,17 +398,23 @@ class AttGAN(object):
                                         test_loader)
 
             if self.cfg.save_checkpoints:
-                utility.checkpoints(epoch_id, self.cfg, gen_trainer,
-                                    "net_G")
-                utility.checkpoints(epoch_id, self.cfg, dis_trainer,
-                                    "net_D")
+                utility.checkpoints(epoch_id, self.cfg, gen_trainer, "net_G")
+                utility.checkpoints(epoch_id, self.cfg, dis_trainer, "net_D")
             # used for continuous evaluation
             if self.cfg.enable_ce:
-                device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
-                print("kpis\tattgan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
-                print("kpis\tattgan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
-                print("kpis\tattgan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
-                print("kpis\tattgan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
-                print("kpis\tattgan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
-                print("kpis\tattgan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
-                print("kpis\tattgan_Batch_time_cost_card{}\t{}".format(device_num,batch_time)) 
+                device_num = fluid.core.get_cuda_device_count(
+                ) if self.cfg.use_gpu else 1
+                print("kpis\tattgan_g_loss_fake_card{}\t{}".format(
+                    device_num, g_loss_fake[0]))
+                print("kpis\tattgan_g_loss_rec_card{}\t{}".format(
+                    device_num, g_loss_rec[0]))
+                print("kpis\tattgan_g_loss_cls_card{}\t{}".format(
+                    device_num, g_loss_cls[0]))
+                print("kpis\tattgan_d_loss_real_card{}\t{}".format(
+                    device_num, d_loss_real[0]))
+                print("kpis\tattgan_d_loss_fake_card{}\t{}".format(
+                    device_num, d_loss_fake[0]))
+                print("kpis\tattgan_d_loss_gp_card{}\t{}".format(device_num,
+                                                                 d_loss_gp[0]))
+                print("kpis\tattgan_Batch_time_cost_card{}\t{}".format(
+                    device_num, batch_time))

PaddleCV/gan/trainer/CGAN.py

@@ -37,8 +37,9 @@ class GTrainer():
             self.fake = model.network_G(input, conditions, name="G")
             self.infer_program = self.program.clone(for_test=True)
             d_fake = model.network_D(self.fake, conditions, name="D")
-            fake_labels = fluid.layers.fill_constant_batch_size_like(
-                input=input, dtype='float32', shape=[-1, 1], value=1.0)
+            batch = fluid.layers.shape(input)[0]
+            fake_labels = fluid.layers.fill_constant(
+                dtype='float32', shape=[batch, 1], value=1.0)
             self.g_loss = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
                     x=d_fake, label=fake_labels))

PaddleCV/gan/trainer/DCGAN.py

@@ -38,8 +38,9 @@ class GTrainer():
             self.fake = model.network_G(input, name='G')
             self.infer_program = self.program.clone(for_test=True)
             d_fake = model.network_D(self.fake, name="D")
-            fake_labels = fluid.layers.fill_constant_batch_size_like(
-                input, dtype='float32', shape=[-1, 1], value=1.0)
+            batch = fluid.layers.shape(input)[0]
+            fake_labels = fluid.layers.fill_constant(
+                dtype='float32', shape=[batch, 1], value=1.0)
             self.g_loss = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
                     x=d_fake, label=fake_labels))

PaddleCV/gan/trainer/Pix2pix.py

@@ -33,10 +33,10 @@ class GTrainer():
             self.infer_program = self.program.clone()
             AB = fluid.layers.concat([input_A, self.fake_B], 1)
             self.pred = model.network_D(AB, "discriminator", cfg)
+            batch = fluid.layers.shape(self.pred)[0]
             if cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred,
-                    shape=self.pred.shape,
+                ones = fluid.layers.fill_constant(
+                    shape=[batch] + list(self.pred.shape[1:]),
                     value=1,
                     dtype='float32')
                 self.g_loss_gan = fluid.layers.reduce_mean(
@@ -49,9 +49,8 @@ class GTrainer():
                     self.pred,
                     [-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
                     inplace=True)
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred,
-                    shape=self.pred.shape,
+                ones = fluid.layers.fill_constant(
+                    shape=[batch] + list(self.pred.shape[1:]),
                     value=1,
                     dtype='float32')
                 self.g_loss_gan = fluid.layers.mean(
@@ -106,10 +105,10 @@ class DTrainer():
                 self.real_AB, "discriminator", cfg=cfg)
             self.pred_fake = model.network_D(
                 self.fake_AB, "discriminator", cfg=cfg)
+            batch = fluid.layers.shape(input_A)[0]
             if cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_real,
-                    shape=self.pred_real.shape,
+                ones = fluid.layers.fill_constant(
+                    shape=[batch] + list(self.pred_real.shape[1:]),
                     value=1,
                     dtype='float32')
                 self.d_loss_real = fluid.layers.reduce_mean(
@@ -128,14 +127,12 @@ class DTrainer():
                     self.pred_fake,
                     [-1, pred_shape[1] * pred_shape[2] * pred_shape[3]],
                     inplace=True)
-                zeros = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_fake,
-                    shape=self.pred_fake.shape,
+                zeros = fluid.layers.fill_constant(
+                    shape=[batch] + list(self.pred_fake.shape[1:]),
                     value=0,
                     dtype='float32')
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_real,
-                    shape=self.pred_real.shape,
+                ones = fluid.layers.fill_constant(
+                    shape=[batch] + list(self.pred_real.shape[1:]),
                     value=1,
                     dtype='float32')
                 self.d_loss_real = fluid.layers.mean(
@@ -283,7 +280,8 @@ class Pix2pix(object):
                 devices_num = utility.get_device_num(self.cfg)
                 fake_per_device = int(len(fake_B_tmp) / devices_num)
                 for dev in range(devices_num):
-                    tensor[dev]['input_fake'] = fake_B_tmp[dev * fake_per_device : (dev+1) * fake_per_device]
+                    tensor[dev]['input_fake'] = fake_B_tmp[
+                        dev * fake_per_device:(dev + 1) * fake_per_device]
 
                 # optimize the discriminator network
                 d_loss_real, d_loss_fake = exe.run(dis_trainer_program,
@@ -338,10 +336,8 @@ class Pix2pix(object):
                     A_id2name=self.id2name)
 
             if self.cfg.save_checkpoints:
-                utility.checkpoints(epoch_id, self.cfg, gen_trainer,
-                                    "net_G")
-                utility.checkpoints(epoch_id, self.cfg, dis_trainer,
-                                    "net_D")
+                utility.checkpoints(epoch_id, self.cfg, gen_trainer, "net_G")
+                utility.checkpoints(epoch_id, self.cfg, dis_trainer, "net_D")
         if self.cfg.enable_ce:
             device_num = fluid.core.get_cuda_device_count(
             ) if self.cfg.use_gpu else 1

PaddleCV/gan/trainer/SPADE.py

@@ -144,11 +144,9 @@ class DTrainer():
             #####gan loss
             self.gan_loss_fake = 0
             for pred_i in self.pred_fake:
-                zeros = fluid.layers.fill_constant_batch_size_like(
-                    input=pred_i[-1],
-                    shape=pred_i[-1].shape,
-                    value=0,
-                    dtype='float32')
+                pred_shape = fluid.layers.shape(pred_i[-1])
+                zeros = fluid.layers.fill_constant(
+                    shape=pred_shape, value=0, dtype='float32')
                 if isinstance(pred_i, list):
                     pred_i = pred_i[-1]
                 minval = fluid.layers.elementwise_min(-1 * pred_i - 1, zeros)
@@ -158,11 +156,9 @@ class DTrainer():
 
             self.gan_loss_real = 0
             for pred_i in self.pred_real:
-                zeros = fluid.layers.fill_constant_batch_size_like(
-                    input=pred_i[-1],
-                    shape=pred_i[-1].shape,
-                    value=0,
-                    dtype='float32')
+                pred_shape = fluid.layers.shape(pred_i[-1])
+                zeros = fluid.layers.fill_constant(
+                    shape=pred_shape, value=0, dtype='float32')
                 if isinstance(pred_i, list):
                     pred_i = pred_i[-1]
                 minval = fluid.layers.elementwise_min(pred_i - 1, zeros)
@@ -298,7 +294,7 @@ class SPADE(object):
             name='input_fake', shape=data_shape, dtype='float32')
         # used for continuous evaluation
         if self.cfg.enable_ce:
-          fluid.default_startup_program().random_seed = 90
+            fluid.default_startup_program().random_seed = 90
 
         gen_trainer = GTrainer(input_A, input_B, input_C, self.cfg,
                                self.batch_num)
@@ -348,7 +344,7 @@ class SPADE(object):
         if self.cfg.enable_ce:
             gen_trainer_program.random_seed = 90
             dis_trainer_program.random_seed = 90
-        
+
         t_time = 0
 
         for epoch_id in range(self.cfg.epoch):
@@ -422,16 +418,21 @@ class SPADE(object):
                     A_id2name=self.id2name)
 
             if self.cfg.save_checkpoints:
-                utility.checkpoints(epoch_id, self.cfg, gen_trainer,
-                                    "net_G")
-                utility.checkpoints(epoch_id, self.cfg, dis_trainer,
-                                    "net_D")
+                utility.checkpoints(epoch_id, self.cfg, gen_trainer, "net_G")
+                utility.checkpoints(epoch_id, self.cfg, dis_trainer, "net_D")
             # used for continuous evaluation
             if self.cfg.enable_ce:
-                device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
-                print("kpis\tspade_g_loss_gan_card{}\t{}".format(device_num, g_loss_gan[0]))
-                print("kpis\tspade_g_loss_vgg_card{}\t{}".format(device_num,g_loss_vgg[0]))
-                print("kpis\tspade_g_loss_feat_card{}\t{}".format(device_num,g_loss_feat[0]))
-                print("kpis\tspade_d_loss_real_card{}\t{}".format(device_num,d_loss_real[0]))
-                print("kpis\tspade_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
-                print("kpis\tspade_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
+                device_num = fluid.core.get_cuda_device_count(
+                ) if self.cfg.use_gpu else 1
+                print("kpis\tspade_g_loss_gan_card{}\t{}".format(device_num,
+                                                                 g_loss_gan[0]))
+                print("kpis\tspade_g_loss_vgg_card{}\t{}".format(device_num,
+                                                                 g_loss_vgg[0]))
+                print("kpis\tspade_g_loss_feat_card{}\t{}".format(
+                    device_num, g_loss_feat[0]))
+                print("kpis\tspade_d_loss_real_card{}\t{}".format(
+                    device_num, d_loss_real[0]))
+                print("kpis\tspade_d_loss_fake_card{}\t{}".format(
+                    device_num, d_loss_fake[0]))
+                print("kpis\tspade_Batch_time_cost_card{}\t{}".format(
+                    device_num, batch_time))

PaddleCV/gan/trainer/STGAN.py

@@ -45,11 +45,9 @@ class GTrainer():
                 self.g_loss_fake = -1 * fluid.layers.mean(self.pred_fake)
             #lsgan
             elif cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_fake,
-                    shape=self.pred_fake.shape,
-                    value=1.0,
-                    dtype='float32')
+                fake_shape = fluid.layers.shape(self.pred_fake)
+                ones = fluid.layers.fill_constant(
+                    shape=fake_shape, value=1.0, dtype='float32')
                 self.g_loss_fake = fluid.layers.mean(
                     fluid.layers.square(
                         fluid.layers.elementwise_sub(
@@ -108,11 +106,9 @@ class DTrainer():
                 self.d_loss = self.d_loss_real + self.d_loss_fake + 1.0 * self.d_loss_cls + cfg.lambda_gp * self.d_loss_gp
             #lsgan
             elif cfg.gan_mode == "lsgan":
-                ones = fluid.layers.fill_constant_batch_size_like(
-                    input=self.pred_real,
-                    shape=self.pred_real.shape,
-                    value=1.0,
-                    dtype='float32')
+                real_shape = fluid.layers.shape(self.pred_real)
+                ones = fluid.layers.fill_constant(
+                    shape=real_shape, value=1.0, dtype='float32')
                 self.d_loss_real = fluid.layers.mean(
                     fluid.layers.square(
                         fluid.layers.elementwise_sub(
@@ -149,31 +145,30 @@ class DTrainer():
 
     def gradient_penalty(self, f, real, fake=None, cfg=None, name=None):
         def _interpolate(a, b=None):
+            a_shape = fluid.layers.shape(a)
             if b is None:
                 if cfg.enable_ce:
-                   beta = fluid.layers.uniform_random_batch_size_like(
-                       input=a, shape=a.shape, min=0.0, max=1.0, seed=1)
+                    beta = fluid.layers.uniform_random(
+                        shape=a_shape, min=0.0, max=1.0, seed=1)
                 else:
-                   beta = fluid.layers.uniform_random_batch_size_like(
-                       input=a, shape=a.shape, min=0.0, max=1.0)
-                   
+                    beta = fluid.layers.uniform_random(
+                        shape=a_shape, min=0.0, max=1.0)
+
                 mean = fluid.layers.reduce_mean(
-                    a, dim=list(range(len(a.shape))), keep_dim=True)
+                    a, dim=list(range(len(a.shape))))
                 input_sub_mean = fluid.layers.elementwise_sub(a, mean, axis=0)
                 var = fluid.layers.reduce_mean(
                     fluid.layers.square(input_sub_mean),
-                    dim=list(range(len(a.shape))),
-                    keep_dim=True)
+                    dim=list(range(len(a.shape))))
                 b = beta * fluid.layers.sqrt(var) * 0.5 + a
-            shape = [a.shape[0]]
             if cfg.enable_ce:
-                alpha = fluid.layers.uniform_random_batch_size_like(
-                    input=a, shape=shape, min=0.0, max=1.0, seed=1)
-            else:    
-                alpha = fluid.layers.uniform_random_batch_size_like(
-                    input=a, shape=shape, min=0.0, max=1.0)
+                alpha = fluid.layers.uniform_random(
+                    shape=a_shape[0], min=0.0, max=1.0, seed=1)
+            else:
+                alpha = fluid.layers.uniform_random(
+                    shape=a_shape[0], min=0.0, max=1.0)
 
-            inner = fluid.layers.elementwise_mul((b-a), alpha, axis=0) + a
+            inner = fluid.layers.elementwise_mul((b - a), alpha, axis=0) + a
             return inner
 
         x = _interpolate(real, fake)
@@ -221,7 +216,10 @@ class STGAN(object):
             default=1024,
             help="the base fc dim in discriminator")
         parser.add_argument(
-            '--use_gru', type=ast.literal_eval, default=True, help="whether to use GRU")
+            '--use_gru',
+            type=ast.literal_eval,
+            default=True,
+            help="whether to use GRU")
         parser.add_argument(
             '--lambda_cls',
             type=float,
@@ -345,7 +343,7 @@ class STGAN(object):
         if self.cfg.enable_ce:
             gen_trainer_program.random_seed = 90
             dis_trainer_program.random_seed = 90
- 
+
         t_time = 0
 
         total_train_batch = 0  # used for benchmark
@@ -353,7 +351,7 @@ class STGAN(object):
         for epoch_id in range(self.cfg.epoch):
             batch_id = 0
             for data in loader():
-                if self.cfg.max_iter and total_train_batch == self.cfg.max_iter: # used for benchmark
+                if self.cfg.max_iter and total_train_batch == self.cfg.max_iter:  # used for benchmark
                     return
                 s_time = time.time()
                 # optimize the discriminator network
@@ -389,7 +387,7 @@ class STGAN(object):
                 sys.stdout.flush()
                 batch_id += 1
                 if self.cfg.enable_ce and batch_id == 100:
-                   break
+                    break
 
                 total_train_batch += 1  # used for benchmark
                 # profiler tools
@@ -418,19 +416,27 @@ class STGAN(object):
                                         test_loader)
 
             if self.cfg.save_checkpoints:
-                utility.checkpoints(epoch_id, self.cfg, gen_trainer,
-                                    "net_G")
-                utility.checkpoints(epoch_id, self.cfg, dis_trainer,
-                                    "net_D")
+                utility.checkpoints(epoch_id, self.cfg, gen_trainer, "net_G")
+                utility.checkpoints(epoch_id, self.cfg, dis_trainer, "net_D")
             # used for continuous evaluation
             if self.cfg.enable_ce:
-                device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
-                print("kpis\tstgan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
-                print("kpis\tstgan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
-                print("kpis\tstgan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
-                print("kpis\tstgan_d_loss_card{}\t{}".format(device_num, d_loss[0]))
-                print("kpis\tstgan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
-                print("kpis\tstgan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
-                print("kpis\tstgan_d_loss_cls_card{}\t{}".format(device_num, d_loss_cls[0]))
-                print("kpis\tstgan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
-                print("kpis\tstgan_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
+                device_num = fluid.core.get_cuda_device_count(
+                ) if self.cfg.use_gpu else 1
+                print("kpis\tstgan_g_loss_fake_card{}\t{}".format(
+                    device_num, g_loss_fake[0]))
+                print("kpis\tstgan_g_loss_rec_card{}\t{}".format(device_num,
+                                                                 g_loss_rec[0]))
+                print("kpis\tstgan_g_loss_cls_card{}\t{}".format(device_num,
+                                                                 g_loss_cls[0]))
+                print("kpis\tstgan_d_loss_card{}\t{}".format(device_num, d_loss[
+                    0]))
+                print("kpis\tstgan_d_loss_real_card{}\t{}".format(
+                    device_num, d_loss_real[0]))
+                print("kpis\tstgan_d_loss_fake_card{}\t{}".format(
+                    device_num, d_loss_fake[0]))
+                print("kpis\tstgan_d_loss_cls_card{}\t{}".format(device_num,
+                                                                 d_loss_cls[0]))
+                print("kpis\tstgan_d_loss_gp_card{}\t{}".format(device_num,
+                                                                d_loss_gp[0]))
+                print("kpis\tstgan_Batch_time_cost_card{}\t{}".format(
+                    device_num, batch_time))

PaddleCV/gan/trainer/StarGAN.py

@@ -149,15 +149,17 @@ class DTrainer():
 
     def gradient_penalty(self, f, real, fake, cfg=None, name=None):
         def _interpolate(a, b):
-            shape = [a.shape[0]]
+            a_shape = fluid.layers.shape(a)
             if cfg.enable_ce:
-                  alpha = fluid.layers.uniform_random_batch_size_like(
-                    input=a, shape=shape, min=0.0, max=1.0, seed=1)
-            else:      
-                  alpha = fluid.layers.uniform_random_batch_size_like(
-                    input=a, shape=shape, min=0.0, max=1.0)
+                alpha = fluid.layers.uniform_random(
+                    shape=[a_shape[0]], min=0.0, max=1.0, seed=1)
+            else:
+                alpha = fluid.layers.uniform_random(
+                    shape=[a_shape[0]], min=0.0, max=1.0)
 
-            inner = fluid.layers.elementwise_mul(b, (1.0-alpha), axis=0) + fluid.layers.elementwise_mul(a, alpha, axis=0)
+            inner = fluid.layers.elementwise_mul(
+                b, (1.0 - alpha), axis=0) + fluid.layers.elementwise_mul(
+                    a, alpha, axis=0)
             return inner
 
         x = _interpolate(real, fake)
@@ -316,7 +318,7 @@ class StarGAN(object):
         for epoch_id in range(self.cfg.epoch):
             batch_id = 0
             for data in loader():
-                if self.cfg.max_iter and total_train_batch == self.cfg.max_iter: # used for benchmark
+                if self.cfg.max_iter and total_train_batch == self.cfg.max_iter:  # used for benchmark
                     return
                 s_time = time.time()
                 d_loss_real, d_loss_fake, d_loss, d_loss_cls, d_loss_gp = exe.run(
@@ -355,7 +357,7 @@ class StarGAN(object):
                 batch_id += 1
                 # used for ce
                 if self.cfg.enable_ce and batch_id == 100:
-                   break
+                    break
 
                 total_train_batch += 1  # used for benchmark
                 # profiler tools
@@ -380,22 +382,28 @@ class StarGAN(object):
                     if self.cfg.use_gpu else fluid.cpu_places())
                 test_program = gen_trainer.infer_program
                 utility.save_test_image(epoch_id, self.cfg, exe, place,
-                                        test_program, gen_trainer,
-                                        test_loader)
+                                        test_program, gen_trainer, test_loader)
 
             if self.cfg.save_checkpoints:
-                utility.checkpoints(epoch_id, self.cfg, gen_trainer,
-                                    "net_G")
-                utility.checkpoints(epoch_id, self.cfg, dis_trainer,
-                                    "net_D")
+                utility.checkpoints(epoch_id, self.cfg, gen_trainer, "net_G")
+                utility.checkpoints(epoch_id, self.cfg, dis_trainer, "net_D")
             # used for continuous evaluation
             if self.cfg.enable_ce:
-                device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
-                print("kpis\tstargan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
-                print("kpis\tstargan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
-                print("kpis\tstargan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
-                print("kpis\tstargan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
-                print("kpis\tstargan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
-                print("kpis\tstargan_d_loss_cls_card{}\t{}".format(device_num, d_loss_cls[0]))
-                print("kpis\tstargan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
-                print("kpis\tstargan_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
+                device_num = fluid.core.get_cuda_device_count(
+                ) if self.cfg.use_gpu else 1
+                print("kpis\tstargan_g_loss_fake_card{}\t{}".format(
+                    device_num, g_loss_fake[0]))
+                print("kpis\tstargan_g_loss_rec_card{}\t{}".format(
+                    device_num, g_loss_rec[0]))
+                print("kpis\tstargan_g_loss_cls_card{}\t{}".format(
+                    device_num, g_loss_cls[0]))
+                print("kpis\tstargan_d_loss_real_card{}\t{}".format(
+                    device_num, d_loss_real[0]))
+                print("kpis\tstargan_d_loss_fake_card{}\t{}".format(
+                    device_num, d_loss_fake[0]))
+                print("kpis\tstargan_d_loss_cls_card{}\t{}".format(
+                    device_num, d_loss_cls[0]))
+                print("kpis\tstargan_d_loss_gp_card{}\t{}".format(device_num,
+                                                                  d_loss_gp[0]))
+                print("kpis\tstargan_Batch_time_cost_card{}\t{}".format(
+                    device_num, batch_time))

PaddleCV/metric_learning/eval.py

@@ -52,8 +52,13 @@ def eval(args):
     assert model_name in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
 
-    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
-    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    image = fluid.layers.data(name='image', shape=[None] + image_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[None, 1], dtype='int64')
+    test_loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image, label],
+                capacity=64,
+                use_double_buffer=True,
+                iterable=True)
 
     # model definition
     model = models.__dict__[model_name]()
@@ -72,16 +77,21 @@ def eval(args):
 
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    test_reader = paddle.batch(reader.test(args), batch_size=args.batch_size, drop_last=False)
-    feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
+    test_loader.set_sample_generator(
+        reader.test(args),
+        batch_size=args.batch_size,
+        drop_last=False,
+        places=place)
 
     fetch_list = [out.name]
 
     f, l = [], []
-    for batch_id, data in enumerate(test_reader()):
+    for batch_id, data in enumerate(test_loader()):
         t1 = time.time()
-        [feas] = exe.run(test_program, fetch_list=fetch_list, feed=feeder.feed(data))
-        label = np.asarray([x[1] for x in data])
+        [feas] = exe.run(test_program, fetch_list=fetch_list, feed=data)
+        label = np.asarray(data[0]['label'])
+        label = np.squeeze(label)
+ 
         f.append(feas)
         l.append(label)
 

PaddleCV/metric_learning/infer.py

@@ -51,7 +51,13 @@ def infer(args):
     assert model_name in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
 
-    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+    image = fluid.layers.data(name='image', shape=[None] + image_shape, dtype='float32')
+
+    infer_loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image],
+                capacity=64,
+                use_double_buffer=True,
+                iterable=True)
 
     # model definition
     model = models.__dict__[model_name]()
@@ -70,13 +76,16 @@ def infer(args):
 
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    infer_reader = paddle.batch(reader.infer(args), batch_size=args.batch_size, drop_last=False)
-    feeder = fluid.DataFeeder(place=place, feed_list=[image])
+    infer_loader.set_sample_generator(
+        reader.test(args),
+        batch_size=args.batch_size,
+        drop_last=False,
+        places=place)
 
     fetch_list = [out.name]
 
-    for batch_id, data in enumerate(infer_reader()):
-        result = exe.run(test_program, fetch_list=fetch_list, feed=feeder.feed(data))
+    for batch_id, data in enumerate(infer_loader()):
+        result = exe.run(test_program, fetch_list=fetch_list, feed=data)
         result = result[0][0].reshape(-1)
         print("Test-{0}-feature: {1}".format(batch_id, result[:5]))
         sys.stdout.flush()

PaddleCV/metric_learning/losses/arcmarginloss.py

@@ -61,7 +61,8 @@ class ArcMarginLoss():
         else:
             phi = self.paddle_where_more_than(cosine, th, phi, cosine-mm)
 
-        one_hot = fluid.layers.one_hot(input=label, depth=out_dim)
+        one_hot = fluid.one_hot(input=label, depth=out_dim)
+        one_hot = fluid.layers.squeeze(input=one_hot, axes=[1])
         output = fluid.layers.elementwise_mul(one_hot, phi) + fluid.layers.elementwise_mul((1.0 - one_hot), cosine)
         output = output * s
         return output

PaddleCV/metric_learning/reader.py

@@ -22,6 +22,7 @@ import functools
 import numpy as np
 import paddle
 from imgtool import process_image
+import paddle.fluid as fluid
 
 random.seed(0)
 
@@ -187,7 +188,7 @@ def createreader(settings, mode):
     keep_order = False if mode != 'train' or settings.loss_name in ['softmax', 'arcmargin'] else True
     image_mapper = functools.partial(process_image,
             mode=mode, color_jitter=False, rotate=False, crop_size=image_size)
-    reader = paddle.reader.xmap_readers(
+    reader = fluid.io.xmap_readers(
             image_mapper, metric_reader, 8, 1000, order=keep_order)
     return reader
 

PaddleCV/metric_learning/train_elem.py

@@ -107,19 +107,16 @@ def build_program(is_train, main_prog, startup_prog, args):
     image_shape = [int(m) for m in args.image_shape.split(",")]
     model = models.__dict__[args.model]()
     with fluid.program_guard(main_prog, startup_prog):
-        if is_train:
-            queue_capacity = 64
-            py_reader = fluid.layers.py_reader(
+        queue_capacity = 64
+        image = fluid.layers.data(
+                name='image', shape=[None] + image_shape, dtype='float32')
+        label = fluid.layers.data(
+                name='label', shape=[None, 1], dtype='int64')
+        loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image, label],
                 capacity=queue_capacity,
-                shapes=[[-1] + image_shape, [-1, 1]],
-                lod_levels=[0, 0],
-                dtypes=["float32", "int64"],
-                use_double_buffer=True)
-            image, label = fluid.layers.read_file(py_reader)
-        else:
-            image = fluid.layers.data(
-                name='image', shape=image_shape, dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+                use_double_buffer=True,
+                iterable=True)
 
         with fluid.unique_name.guard():
             avg_cost, acc_top1, acc_top5, out = net_config(image, label, model,
@@ -137,9 +134,9 @@ def build_program(is_train, main_prog, startup_prog, args):
         main_prog = main_prog.clone(for_test=True)
     """
     if is_train:
-        return py_reader, avg_cost, acc_top1, acc_top5, global_lr
+        return loader, avg_cost, acc_top1, acc_top5, global_lr
     else:
-        return out, image, label
+        return loader, out
 
 
 def train_async(args):
@@ -163,12 +160,12 @@ def train_async(args):
         train_prog.random_seed = 1000
         tmp_prog.random_seed = 1000
 
-    train_py_reader, train_cost, train_acc1, train_acc5, global_lr = build_program(
+    train_loader, train_cost, train_acc1, train_acc5, global_lr = build_program(
         is_train=True,
         main_prog=train_prog,
         startup_prog=startup_prog,
         args=args)
-    test_feas, image, label = build_program(
+    test_loader, test_feas = build_program(
         is_train=False,
         main_prog=tmp_prog,
         startup_prog=startup_prog,
@@ -182,6 +179,11 @@ def train_async(args):
 
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
+    num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
+    if num_trainers <= 1 and args.use_gpu:
+        places = fluid.framework.cuda_places()
+    else:
+        places = place
 
     exe.run(startup_prog)
 
@@ -206,12 +208,17 @@ def train_async(args):
     train_batch_size = args.train_batch_size // devicenum
     test_batch_size = args.test_batch_size
 
-    train_reader = paddle.batch(
-        reader.train(args), batch_size=train_batch_size, drop_last=True)
-    test_reader = paddle.batch(
-        reader.test(args), batch_size=test_batch_size, drop_last=False)
-    test_feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
-    train_py_reader.decorate_paddle_reader(train_reader)
+    train_loader.set_sample_generator(
+        reader.train(args), 
+        batch_size=train_batch_size,
+        drop_last=True,
+        places=places)
+
+    test_loader.set_sample_generator(
+        reader.test(args),
+        batch_size=test_batch_size,
+        drop_last=False,
+        places=place)
 
     train_exe = fluid.ParallelExecutor(
         main_program=train_prog,
@@ -219,72 +226,76 @@ def train_async(args):
         loss_name=train_cost.name)
 
     totalruntime = 0
-    train_py_reader.start()
     iter_no = 0
     train_info = [0, 0, 0, 0]
     while iter_no <= args.total_iter_num:
-        t1 = time.time()
-        lr, loss, acc1, acc5 = train_exe.run(fetch_list=train_fetch_list)
-        t2 = time.time()
-        period = t2 - t1
-        lr = np.mean(np.array(lr))
-        train_info[0] += np.mean(np.array(loss))
-        train_info[1] += np.mean(np.array(acc1))
-        train_info[2] += np.mean(np.array(acc5))
-        train_info[3] += 1
-        if iter_no % args.display_iter_step == 0:
-            avgruntime = totalruntime / args.display_iter_step
-            avg_loss = train_info[0] / train_info[3]
-            avg_acc1 = train_info[1] / train_info[3]
-            avg_acc5 = train_info[2] / train_info[3]
-            print("[%s] trainbatch %d, lr %.6f, loss %.6f, "\
+        for train_batch in train_loader():
+            t1 = time.time()
+            lr, loss, acc1, acc5 = train_exe.run(
+                                       feed=train_batch,
+                                       fetch_list=train_fetch_list)
+            t2 = time.time()
+            period = t2 - t1
+            lr = np.mean(np.array(lr))
+            train_info[0] += np.mean(np.array(loss))
+            train_info[1] += np.mean(np.array(acc1))
+            train_info[2] += np.mean(np.array(acc5))
+            train_info[3] += 1
+            if iter_no % args.display_iter_step == 0:
+                avgruntime = totalruntime / args.display_iter_step
+                avg_loss = train_info[0] / train_info[3]
+                avg_acc1 = train_info[1] / train_info[3]
+                avg_acc5 = train_info[2] / train_info[3]
+                print("[%s] trainbatch %d, lr %.6f, loss %.6f, "\
                     "acc1 %.4f, acc5 %.4f, time %2.2f sec" % \
                     (fmt_time(), iter_no, lr, avg_loss, avg_acc1, avg_acc5, avgruntime))
-            sys.stdout.flush()
-            totalruntime = 0
-        if iter_no % 1000 == 0:
-            train_info = [0, 0, 0, 0]
-
-        totalruntime += period
-
-        if iter_no % args.test_iter_step == 0 and iter_no != 0:
-            f, l = [], []
-            for batch_id, data in enumerate(test_reader()):
-                t1 = time.time()
-                [feas] = exe.run(test_prog,
-                                 fetch_list=test_fetch_list,
-                                 feed=test_feeder.feed(data))
-                label = np.asarray([x[1] for x in data])
-                f.append(feas)
-                l.append(label)
-
-                t2 = time.time()
-                period = t2 - t1
-                if batch_id % 20 == 0:
-                    print("[%s] testbatch %d, time %2.2f sec" % \
+                sys.stdout.flush()
+                totalruntime = 0
+            if iter_no % 1000 == 0:
+                train_info = [0, 0, 0, 0]
+
+            totalruntime += period
+
+            if iter_no % args.test_iter_step == 0 and iter_no != 0:
+                f, l = [], []
+                for batch_id, test_batch in enumerate(test_loader()):
+                    t1 = time.time()
+                    [feas] = exe.run(test_prog,
+                                 feed=test_batch,
+                                 fetch_list=test_fetch_list)
+
+                    label = np.asarray(test_batch[0]['label'])
+                    label = np.squeeze(label)
+                    f.append(feas)
+                    l.append(label)
+
+                    t2 = time.time()
+                    period = t2 - t1
+                    if batch_id % 20 == 0:
+                        print("[%s] testbatch %d, time %2.2f sec" % \
                             (fmt_time(), batch_id, period))
 
-            f = np.vstack(f)
-            l = np.hstack(l)
-            recall = recall_topk(f, l, k=1)
-            print("[%s] test_img_num %d, trainbatch %d, test_recall %.5f" % \
-                    (fmt_time(), len(f), iter_no, recall))
-            sys.stdout.flush()
-
-        if iter_no % args.save_iter_step == 0 and iter_no != 0:
-            model_path = os.path.join(model_save_dir + '/' + model_name,
-                                      str(iter_no))
-            if not os.path.isdir(model_path):
-                os.makedirs(model_path)
-            fluid.io.save_persistables(exe, model_path, main_program=train_prog)
-
-        iter_no += 1
-
-    # This is for continuous evaluation only
-    if args.enable_ce:
-        # Use the mean cost/acc for training
-        print("kpis\ttrain_cost\t{}".format(avg_loss))
-        print("kpis\ttest_recall\t{}".format(recall))
+                f = np.vstack(f)
+                l = np.hstack(l)
+                recall = recall_topk(f, l, k=1)
+                print("[%s] test_img_num %d, trainbatch %d, test_recall %.5f" % \
+                        (fmt_time(), len(f), iter_no, recall))
+                sys.stdout.flush()
+
+            if iter_no % args.save_iter_step == 0 and iter_no != 0:
+                model_path = os.path.join(model_save_dir + '/' + model_name,
+                                          str(iter_no))
+                if not os.path.isdir(model_path):
+                    os.makedirs(model_path)
+                fluid.io.save_persistables(exe, model_path, main_program=train_prog)
+
+            iter_no += 1
+
+        # This is for continuous evaluation only
+        if args.enable_ce:
+            # Use the mean cost/acc for training
+            print("kpis\ttrain_cost\t{}".format(avg_loss))
+            print("kpis\ttest_recall\t{}".format(recall))
 
 
 def initlogging():

PaddleCV/metric_learning/train_pair.py

@@ -114,19 +114,16 @@ def build_program(is_train, main_prog, startup_prog, args):
     image_shape = [int(m) for m in args.image_shape.split(",")]
     model = models.__dict__[args.model]()
     with fluid.program_guard(main_prog, startup_prog):
-        if is_train:
-            queue_capacity = 64
-            py_reader = fluid.layers.py_reader(
+        queue_capacity = 64
+        image = fluid.layers.data(
+                name='image', shape=[None] + image_shape, dtype='float32')
+        label = fluid.layers.data(
+                name='label', shape=[None, 1], dtype='int64')
+        loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image, label],
                 capacity=queue_capacity,
-                shapes=[[-1] + image_shape, [-1, 1]],
-                lod_levels=[0, 0],
-                dtypes=["float32", "int64"],
-                use_double_buffer=True)
-            image, label = fluid.layers.read_file(py_reader)
-        else:
-            image = fluid.layers.data(
-                name='image', shape=image_shape, dtype='float32')
-            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+                use_double_buffer=True,
+                iterable=True)
 
         with fluid.unique_name.guard():
             avg_cost, out = net_config(image, label, model, args, is_train)
@@ -143,9 +140,9 @@ def build_program(is_train, main_prog, startup_prog, args):
         main_prog = main_prog.clone(for_test=True)
     """
     if is_train:
-        return py_reader, avg_cost, global_lr, out, label
+        return loader, avg_cost, global_lr, out, label
     else:
-        return out, image, label
+        return loader, out
 
 
 def train_async(args):
@@ -161,12 +158,12 @@ def train_async(args):
     train_prog = fluid.Program()
     tmp_prog = fluid.Program()
 
-    train_py_reader, train_cost, global_lr, train_feas, train_label = build_program(
+    train_loader, train_cost, global_lr, train_feas, train_label = build_program(
         is_train=True,
         main_prog=train_prog,
         startup_prog=startup_prog,
         args=args)
-    test_feas, image, label = build_program(
+    test_loader, test_feas = build_program(
         is_train=False,
         main_prog=tmp_prog,
         startup_prog=startup_prog,
@@ -180,6 +177,11 @@ def train_async(args):
 
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
+    num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
+    if num_trainers <= 1 and args.use_gpu:
+        places = fluid.framework.cuda_places()
+    else:
+        places = place
 
     exe.run(startup_prog)
 
@@ -204,12 +206,17 @@ def train_async(args):
     train_batch_size = args.train_batch_size / devicenum
     test_batch_size = args.test_batch_size
 
-    train_reader = paddle.batch(
-        reader.train(args), batch_size=train_batch_size, drop_last=True)
-    test_reader = paddle.batch(
-        reader.test(args), batch_size=test_batch_size, drop_last=False)
-    test_feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
-    train_py_reader.decorate_paddle_reader(train_reader)
+    train_loader.set_sample_generator(
+        reader.train(args), 
+        batch_size=train_batch_size,
+        drop_last=True,
+        places=places)
+
+    test_loader.set_sample_generator(
+        reader.test(args),
+        batch_size=test_batch_size,
+        drop_last=False,
+        places=place)
 
     train_exe = fluid.ParallelExecutor(
         main_program=train_prog,
@@ -217,64 +224,68 @@ def train_async(args):
         loss_name=train_cost.name)
 
     totalruntime = 0
-    train_py_reader.start()
     iter_no = 0
     train_info = [0, 0, 0]
     while iter_no <= args.total_iter_num:
-        t1 = time.time()
-        lr, loss, feas, label = train_exe.run(fetch_list=train_fetch_list)
-        t2 = time.time()
-        period = t2 - t1
-        lr = np.mean(np.array(lr))
-        train_info[0] += np.mean(np.array(loss))
-        train_info[1] += recall_topk(feas, label, k=1)
-        train_info[2] += 1
-        if iter_no % args.display_iter_step == 0:
-            avgruntime = totalruntime / args.display_iter_step
-            avg_loss = train_info[0] / train_info[2]
-            avg_recall = train_info[1] / train_info[2]
-            print("[%s] trainbatch %d, lr %.6f, loss %.6f, "\
+        for train_batch in train_loader():
+            t1 = time.time()
+            lr, loss, feas, label = train_exe.run(
+                                        feed=train_batch,
+                                        fetch_list=train_fetch_list)
+            t2 = time.time()
+            period = t2 - t1
+            lr = np.mean(np.array(lr))
+            train_info[0] += np.mean(np.array(loss))
+            train_info[1] += recall_topk(feas, label, k=1)
+            train_info[2] += 1
+            if iter_no % args.display_iter_step == 0:
+                avgruntime = totalruntime / args.display_iter_step
+                avg_loss = train_info[0] / train_info[2]
+                avg_recall = train_info[1] / train_info[2]
+                print("[%s] trainbatch %d, lr %.6f, loss %.6f, "\
                     "recall %.4f, time %2.2f sec" % \
                     (fmt_time(), iter_no, lr, avg_loss, avg_recall, avgruntime))
-            sys.stdout.flush()
-            totalruntime = 0
-        if iter_no % 1000 == 0:
-            train_info = [0, 0, 0]
-
-        totalruntime += period
-
-        if iter_no % args.test_iter_step == 0 and iter_no != 0:
-            f, l = [], []
-            for batch_id, data in enumerate(test_reader()):
-                t1 = time.time()
-                [feas] = exe.run(test_prog,
-                                 fetch_list=test_fetch_list,
-                                 feed=test_feeder.feed(data))
-                label = np.asarray([x[1] for x in data])
-                f.append(feas)
-                l.append(label)
-
-                t2 = time.time()
-                period = t2 - t1
-                if batch_id % 20 == 0:
-                    print("[%s] testbatch %d, time %2.2f sec" % \
+                sys.stdout.flush()
+                totalruntime = 0
+            if iter_no % 1000 == 0:
+                train_info = [0, 0, 0]
+
+            totalruntime += period
+
+            if iter_no % args.test_iter_step == 0 and iter_no != 0:
+                f, l = [], []
+                for batch_id, test_batch in enumerate(test_loader()):
+                    t1 = time.time()
+                    [feas] = exe.run(test_prog,
+                                 feed=test_batch,
+                                 fetch_list=test_fetch_list)
+
+                    label = np.asarray(test_batch[0]['label'])
+                    label = np.squeeze(label)
+                    f.append(feas)
+                    l.append(label)
+
+                    t2 = time.time()
+                    period = t2 - t1
+                    if batch_id % 20 == 0:
+                        print("[%s] testbatch %d, time %2.2f sec" % \
                             (fmt_time(), batch_id, period))
 
-            f = np.vstack(f)
-            l = np.hstack(l)
-            recall = recall_topk(f, l, k=1)
-            print("[%s] test_img_num %d, trainbatch %d, test_recall %.5f" % \
+                f = np.vstack(f)
+                l = np.hstack(l)
+                recall = recall_topk(f, l, k=1)
+                print("[%s] test_img_num %d, trainbatch %d, test_recall %.5f" % \
                     (fmt_time(), len(f), iter_no, recall))
-            sys.stdout.flush()
+                sys.stdout.flush()
 
-        if iter_no % args.save_iter_step == 0 and iter_no != 0:
-            model_path = os.path.join(model_save_dir + '/' + model_name,
+            if iter_no % args.save_iter_step == 0 and iter_no != 0:
+                model_path = os.path.join(model_save_dir + '/' + model_name,
                                       str(iter_no))
-            if not os.path.isdir(model_path):
-                os.makedirs(model_path)
-            fluid.io.save_persistables(exe, model_path, main_program=train_prog)
+                if not os.path.isdir(model_path):
+                    os.makedirs(model_path)
+                fluid.io.save_persistables(exe, model_path, main_program=train_prog)
 
-        iter_no += 1
+            iter_no += 1
 
 
 def initlogging():

PaddleCV/tracking/pytracking/libs/paddle_utils.py

@@ -211,8 +211,8 @@ def dropout2d(input, prob, is_train=False):
         return input
     channels = input.shape[1]
     keep_prob = 1.0 - prob
-    random_tensor = keep_prob + layers.uniform_random_batch_size_like(
-        input, [-1, channels, 1, 1], min=0., max=1.)
+    random_tensor = np.random.uniform(0, 1, [input.shape[0], channels, 1, 1]).astype(np.float32)
+    random_tensor = keep_prob + dygraph.to_variable(random_tensor)
     binary_tensor = layers.floor(random_tensor)
     output = input / keep_prob * binary_tensor
     return output

PaddleCV/video/README.md

@@ -99,12 +99,19 @@ python train.py --model_name=STNET \
 bash run.sh train STNET ./configs/stnet.yaml
 ```
 
+多卡分布式训练 + GPU视频解码和预处理（仅限TSN模型）
+
+``` bash
+bash run_dist.sh train TSN ./configs/tsn_dist_and_dali.yaml
+```
+
 - 请根据`CUDA_VISIBLE_DEVICES`指定卡数修改`config`文件中的`num_gpus`和`batch_size`配置。
 
 - 使用CPU训练时请在run.sh中设置use\_gpu=False，使用GPU训练时则设置use\_gpu=True
 
 - 上述启动脚本run.sh运行时需要指定任务类型、模型名、配置文件。训练、评估和预测对应的任务类型分别是train，eval和predict。模型名称则是[AttentionCluster, AttentionLSTM, NEXTVLAD, NONLOCAL, STNET, TSN, TSM, CTCN]中的任何一个。配置文件全部在PaddleVideo/configs目录下，根据模型名称选择对应的配置文件即可。具体使用请参见各模型的说明文档。
 
+- 目前针对TSN模型，做了GPU解码和数据预处理的优化，能明显提升训练速度，具体请参考[TSN](./models/tsn/README.md)
 
 ## 模型库结构
 

PaddleCV/video/configs/tsn_dist_and_dali.yaml

+MODEL:
+    name: "TSN"
+    format: "pkl"
+    num_classes: 400
+    seg_num: 3
+    seglen: 1
+    image_mean: [0.485, 0.456, 0.406]
+    image_std: [0.229, 0.224, 0.225]
+    num_layers: 50
+    topk: 5
+
+TRAIN:
+    epoch: 45
+    short_size: 256
+    target_size: 224
+    num_reader_threads: 12
+    buf_size: 1024
+    batch_size: 256
+    use_gpu: True
+    num_gpus: 8
+    filelist: "./data/dataset/kinetics/train_video_file.list"
+    learning_rate: 0.01
+    learning_rate_decay: 0.1
+    l2_weight_decay: 1e-4
+    momentum: 0.9
+    total_videos: 224684
+    num_trainers: 1  # this will be determined by fleet implicitly, no need to set
+    trainer_id: 0    # this will be determined by fleet implicitly, no need to set
+    use_dali: True
+
+VALID:
+    short_size: 256
+    target_size: 224
+    num_reader_threads: 12
+    buf_size: 1024
+    batch_size: 256
+    filelist: "./data/dataset/kinetics/val_video_file.list"
+
+TEST:
+    seg_num: 7
+    short_size: 256
+    target_size: 224
+    num_reader_threads: 12
+    buf_size: 1024
+    batch_size: 16
+    filelist: "./data/dataset/kinetics/test_video_file.list"
+    use_dali: True
+
+INFER:
+    short_size: 256
+    target_size: 224
+    num_reader_threads: 12
+    buf_size: 1024
+    batch_size: 1
+    filelist: "./data/dataset/kinetics/infer_video_file.list"
+    video_path: ""
+    kinetics_labels: "./data/dataset/kinetics_labels.json"

PaddleCV/video/eval.py

@@ -81,6 +81,7 @@ def test(args):
     config = parse_config(args.config)
     test_config = merge_configs(config, 'test', vars(args))
     print_configs(test_config, "Test")
+    use_dali = test_config['TEST'].get('use_dali', False)
 
     # build model
     test_model = models.get_model(args.model_name, test_config, mode='test')
@@ -127,6 +128,10 @@ def test(args):
                                 feed=test_feeder.feed(feat_data),
                                 return_numpy=True)
             test_outs += [vinfo]
+        elif args.model_name == 'TSN' and use_dali:
+            test_outs = exe.run(fetch_list=test_fetch_list,
+                                feed={'image': data[0],
+                                      'label': data[1]})
         else:
             test_outs = exe.run(fetch_list=test_fetch_list,
                                 feed=test_feeder.feed(data))

PaddleCV/video/models/tsn/README.md

@@ -54,6 +54,18 @@ TSN的训练数据采用由DeepMind公布的Kinetics-400动作识别数据集。
 *  权重衰减系数为1e-4
 *  学习率在训练的总epoch数的1/3和2/3时分别做0.1的衰减
 
+**训练速度优化：**
+
+*  使用GPU解码优化视频源文件读取和预处理速度，需要预先安装NVIDIA/DALI
+*  DALI的安装方式请参考NVIDIA/DALI[官方文档](https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/compilation.html#)。由于NVIDIA/DALI提供的VideoReader OP不支持TSN模型的采样方式，请使用[SunGaofeng/DALI](https://github.com/SunGaofeng/DALI)提供的源码，提供了时间维度的稀疏采样方式
+*  使用分布式训练的方式提升多卡加速比
+
+启动脚本为：
+
+``` bash
+bash run_dist.sh train TSN ./configs/tsn_dist_and_dali.yaml
+```
+
 ## 模型评估
 
 可通过如下两种方式进行模型评估:

PaddleCV/video/reader/kinetics_reader.py

@@ -26,6 +26,16 @@ except ImportError:
     from io import BytesIO
 import numpy as np
 import paddle
+
+try:
+    from nvidia.dali.pipeline import Pipeline
+    import nvidia.dali.ops as ops
+    import nvidia.dali.types as types
+    import tempfile
+    from nvidia.dali.plugin.paddle import DALIGenericIterator
+except:
+    print("DALI is not installed, you can improve performance if use DALI")
+
 from PIL import Image, ImageEnhance
 import logging
 
@@ -76,6 +86,13 @@ class KineticsReader(DataReader):
         # set batch size and file list
         self.batch_size = cfg[mode.upper()]['batch_size']
         self.filelist = cfg[mode.upper()]['filelist']
+        # set num_trainers and trainer_id when distributed training is implemented
+        self.num_trainers = self.get_config_from_sec(mode, 'num_trainers', 1)
+        self.trainer_id = self.get_config_from_sec(mode, 'trainer_id', 0)
+        self.use_dali = self.get_config_from_sec(mode, 'use_dali', False)
+        self.dali_mean = cfg.MODEL.image_mean * (self.seg_num * self.seglen)
+        self.dali_std = cfg.MODEL.image_std * (self.seg_num * self.seglen)
+
         if self.mode == 'infer':
             self.video_path = cfg[mode.upper()]['video_path']
         else:
@@ -86,6 +103,10 @@ class KineticsReader(DataReader):
             self.num_reader_threads = 1
 
     def create_reader(self):
+        # if use_dali to improve performance
+        if self.use_dali:
+            return self.build_dali_reader()
+
         # if set video_path for inference mode, just load this single video
         if (self.mode == 'infer') and (self.video_path != ''):
             # load video from file stored at video_path
@@ -201,11 +222,35 @@ class KineticsReader(DataReader):
             return imgs_transform(imgs, mode, seg_num, seglen, \
                          short_size, target_size, img_mean, img_std, name = self.name), ret_label
 
-        def reader():
+        def reader_():
             with open(pickle_list) as flist:
-                lines = [line.strip() for line in flist]
-                if shuffle:
-                    random.shuffle(lines)
+                full_lines = [line.strip() for line in flist]
+                if self.mode == 'train':
+                    if (not hasattr(reader_, 'seed')):
+                        reader_.seed = 0
+                    random.Random(reader_.seed).shuffle(full_lines)
+                    print("reader shuffle seed", reader_.seed)
+                    if reader_.seed is not None:
+                        reader_.seed += 1
+
+                per_node_lines = int(
+                    math.ceil(len(full_lines) * 1.0 / self.num_trainers))
+                total_lines = per_node_lines * self.num_trainers
+
+                # aligned full_lines so that it can evenly divisible
+                full_lines += full_lines[:(total_lines - len(full_lines))]
+                assert len(full_lines) == total_lines
+
+                # trainer get own sample
+                lines = full_lines[self.trainer_id:total_lines:
+                                   self.num_trainers]
+                logger.info("trainerid %d, trainer_count %d" %
+                            (self.trainer_id, self.num_trainers))
+                logger.info(
+                    "read images from %d, length: %d, lines length: %d, total: %d"
+                    % (self.trainer_id * per_node_lines, per_node_lines,
+                       len(lines), len(full_lines)))
+                assert len(lines) == per_node_lines
                 for line in lines:
                     pickle_path = line.strip()
                     yield [pickle_path]
@@ -227,7 +272,251 @@ class KineticsReader(DataReader):
             img_mean=img_mean,
             img_std=img_std)
 
-        return paddle.reader.xmap_readers(mapper, reader, num_threads, buf_size)
+        return paddle.reader.xmap_readers(mapper, reader_, num_threads,
+                                          buf_size)
+
+    def build_dali_reader(self):
+        """
+        build dali training reader
+        """
+
+        def reader_():
+            with open(self.filelist) as flist:
+                full_lines = [line for line in flist]
+                if self.mode == 'train':
+                    if (not hasattr(reader_, 'seed')):
+                        reader_.seed = 0
+                    random.Random(reader_.seed).shuffle(full_lines)
+                    print("reader shuffle seed", reader_.seed)
+                    if reader_.seed is not None:
+                        reader_.seed += 1
+
+                per_node_lines = int(
+                    math.ceil(len(full_lines) * 1.0 / self.num_trainers))
+                total_lines = per_node_lines * self.num_trainers
+
+                # aligned full_lines so that it can evenly divisible
+                full_lines += full_lines[:(total_lines - len(full_lines))]
+                assert len(full_lines) == total_lines
+
+                # trainer get own sample
+                lines = full_lines[self.trainer_id:total_lines:
+                                   self.num_trainers]
+                assert len(lines) == per_node_lines
+
+                logger.info("trainerid %d, trainer_count %d" %
+                            (self.trainer_id, self.num_trainers))
+                logger.info(
+                    "read images from %d, length: %d, lines length: %d, total: %d"
+                    % (self.trainer_id * per_node_lines, per_node_lines,
+                       len(lines), len(full_lines)))
+
+            video_files = ''
+            for item in lines:
+                video_files += item
+            tf = tempfile.NamedTemporaryFile()
+            tf.write(str.encode(video_files))
+            tf.flush()
+            video_files = tf.name
+
+            device_id = int(os.getenv('FLAGS_selected_gpus', 0))
+            print('---------- device id -----------', device_id)
+
+            if self.mode == 'train':
+                pipe = VideoPipe(
+                    batch_size=self.batch_size,
+                    num_threads=1,
+                    device_id=device_id,
+                    file_list=video_files,
+                    sequence_length=self.seg_num * self.seglen,
+                    seg_num=self.seg_num,
+                    seg_length=self.seglen,
+                    resize_shorter_scale=self.short_size,
+                    crop_target_size=self.target_size,
+                    is_training=(self.mode == 'train'),
+                    dali_mean=self.dali_mean,
+                    dali_std=self.dali_std)
+            else:
+                pipe = VideoTestPipe(
+                    batch_size=self.batch_size,
+                    num_threads=1,
+                    device_id=device_id,
+                    file_list=video_files,
+                    sequence_length=self.seg_num * self.seglen,
+                    seg_num=self.seg_num,
+                    seg_length=self.seglen,
+                    resize_shorter_scale=self.short_size,
+                    crop_target_size=self.target_size,
+                    is_training=(self.mode == 'train'),
+                    dali_mean=self.dali_mean,
+                    dali_std=self.dali_std)
+            logger.info(
+                'initializing dataset, it will take several minutes if it is too large .... '
+            )
+            video_loader = DALIGenericIterator(
+                [pipe], ['image', 'label'],
+                len(lines),
+                dynamic_shape=True,
+                auto_reset=True)
+
+            return video_loader
+
+        dali_reader = reader_()
+
+        def ret_reader():
+            for data in dali_reader:
+                yield data[0]['image'], data[0]['label']
+
+        return ret_reader
+
+
+class VideoPipe(Pipeline):
+    def __init__(self,
+                 batch_size,
+                 num_threads,
+                 device_id,
+                 file_list,
+                 sequence_length,
+                 seg_num,
+                 seg_length,
+                 resize_shorter_scale,
+                 crop_target_size,
+                 is_training=False,
+                 initial_prefetch_size=10,
+                 num_shards=1,
+                 shard_id=0,
+                 dali_mean=0.,
+                 dali_std=1.0):
+        super(VideoPipe, self).__init__(batch_size, num_threads, device_id)
+        self.input = ops.VideoReader(
+            device="gpu",
+            file_list=file_list,
+            sequence_length=sequence_length,
+            seg_num=seg_num,
+            seg_length=seg_length,
+            is_training=is_training,
+            num_shards=num_shards,
+            shard_id=shard_id,
+            random_shuffle=is_training,
+            initial_fill=initial_prefetch_size)
+        # the sequece data read by ops.VideoReader is of shape [F, H, W, C]
+        # Because the ops.Resize does not support sequence data, 
+        # it will be transposed into [H, W, F, C], 
+        # then reshaped to [H, W, FC], and then resized like a 2-D image.
+        self.transpose = ops.Transpose(device="gpu", perm=[1, 2, 0, 3])
+        self.reshape = ops.Reshape(
+            device="gpu", rel_shape=[1.0, 1.0, -1], layout='HWC')
+        self.resize = ops.Resize(
+            device="gpu", resize_shorter=resize_shorter_scale)
+        # crops and mirror are applied by ops.CropMirrorNormalize.
+        # Normalization will be implemented in paddle due to the difficulty of dimension broadcast,
+        # It is not sure whether dimension broadcast can be implemented correctly by dali, just take the Paddle Op instead.
+        self.pos_rng_x = ops.Uniform(range=(0.0, 1.0))
+        self.pos_rng_y = ops.Uniform(range=(0.0, 1.0))
+        self.mirror_generator = ops.Uniform(range=(0.0, 1.0))
+        self.cast_mirror = ops.Cast(dtype=types.DALIDataType.INT32)
+        self.crop_mirror_norm = ops.CropMirrorNormalize(
+            device="gpu",
+            crop=[crop_target_size, crop_target_size],
+            mean=dali_mean,
+            std=dali_std)
+        self.reshape_back = ops.Reshape(
+            device="gpu",
+            shape=[
+                seg_num, seg_length * 3, crop_target_size, crop_target_size
+            ],
+            layout='FCHW')
+        self.cast_label = ops.Cast(device="gpu", dtype=types.DALIDataType.INT64)
+
+    def define_graph(self):
+        output, label = self.input(name="Reader")
+        output = self.transpose(output)
+        output = self.reshape(output)
+
+        output = self.resize(output)
+        output = output / 255.
+        pos_x = self.pos_rng_x()
+        pos_y = self.pos_rng_y()
+        mirror_flag = self.mirror_generator()
+        mirror_flag = (mirror_flag > 0.5)
+        mirror_flag = self.cast_mirror(mirror_flag)
+        #output = self.crop(output, crop_pos_x=pos_x, crop_pos_y=pos_y)
+        output = self.crop_mirror_norm(
+            output, crop_pos_x=pos_x, crop_pos_y=pos_y, mirror=mirror_flag)
+        output = self.reshape_back(output)
+        label = self.cast_label(label)
+        return output, label
+
+
+class VideoTestPipe(Pipeline):
+    def __init__(self,
+                 batch_size,
+                 num_threads,
+                 device_id,
+                 file_list,
+                 sequence_length,
+                 seg_num,
+                 seg_length,
+                 resize_shorter_scale,
+                 crop_target_size,
+                 is_training=False,
+                 initial_prefetch_size=10,
+                 num_shards=1,
+                 shard_id=0,
+                 dali_mean=0.,
+                 dali_std=1.0):
+        super(VideoTestPipe, self).__init__(batch_size, num_threads, device_id)
+        self.input = ops.VideoReader(
+            device="gpu",
+            file_list=file_list,
+            sequence_length=sequence_length,
+            seg_num=seg_num,
+            seg_length=seg_length,
+            is_training=is_training,
+            num_shards=num_shards,
+            shard_id=shard_id,
+            random_shuffle=is_training,
+            initial_fill=initial_prefetch_size)
+        # the sequece data read by ops.VideoReader is of shape [F, H, W, C]
+        # Because the ops.Resize does not support sequence data, 
+        # it will be transposed into [H, W, F, C], 
+        # then reshaped to [H, W, FC], and then resized like a 2-D image.
+        self.transpose = ops.Transpose(device="gpu", perm=[1, 2, 0, 3])
+        self.reshape = ops.Reshape(
+            device="gpu", rel_shape=[1.0, 1.0, -1], layout='HWC')
+        self.resize = ops.Resize(
+            device="gpu", resize_shorter=resize_shorter_scale)
+        # crops and mirror are applied by ops.CropMirrorNormalize.
+        # Normalization will be implemented in paddle due to the difficulty of dimension broadcast,
+        # It is not sure whether dimension broadcast can be implemented correctly by dali, just take the Paddle Op instead.
+        self.crop_mirror_norm = ops.CropMirrorNormalize(
+            device="gpu",
+            crop=[crop_target_size, crop_target_size],
+            crop_pos_x=0.5,
+            crop_pos_y=0.5,
+            mirror=0,
+            mean=dali_mean,
+            std=dali_std)
+        self.reshape_back = ops.Reshape(
+            device="gpu",
+            shape=[
+                seg_num, seg_length * 3, crop_target_size, crop_target_size
+            ],
+            layout='FCHW')
+        self.cast_label = ops.Cast(device="gpu", dtype=types.DALIDataType.INT64)
+
+    def define_graph(self):
+        output, label = self.input(name="Reader")
+        output = self.transpose(output)
+        output = self.reshape(output)
+
+        output = self.resize(output)
+        output = output / 255.
+        #output = self.crop(output, crop_pos_x=pos_x, crop_pos_y=pos_y)
+        output = self.crop_mirror_norm(output)
+        output = self.reshape_back(output)
+        label = self.cast_label(label)
+        return output, label
 
 
 def imgs_transform(imgs,

PaddleCV/video/run_dist.sh

+# examples of running programs:
+# bash ./run.sh train CTCN ./configs/ctcn.yaml
+# bash ./run.sh eval NEXTVLAD ./configs/nextvlad.yaml
+# bash ./run.sh predict NONLOCAL ./cofings/nonlocal.yaml
+
+# mode should be one of [train, eval, predict, inference]
+# name should be one of [AttentionCluster, AttentionLSTM, NEXTVLAD, NONLOCAL, TSN, TSM, STNET, CTCN]
+# configs should be ./configs/xxx.yaml
+
+mode=$1
+name=$2
+configs=$3
+
+pretrain="" # set pretrain model path if needed
+resume="" # set pretrain model path if needed
+save_dir="./data/checkpoints"
+save_inference_dir="./data/inference_model"
+use_gpu=True
+fix_random_seed=False
+log_interval=1
+valid_interval=1
+
+weights="" #set the path of weights to enable eval and predicut, just ignore this when training
+
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+#export CUDA_VISIBLE_DEVICES=0,1,2,3
+#export CUDA_VISIBLE_DEVICES=0
+export FLAGS_fast_eager_deletion_mode=1
+export FLAGS_eager_delete_tensor_gb=0.0
+export FLAGS_fraction_of_gpu_memory_to_use=0.98
+
+if [ "$mode"x == "train"x ]; then
+    echo $mode $name $configs $resume $pretrain
+    if [ "$resume"x != ""x ]; then
+        python -m paddle.distributed.launch --log_dir=log \
+                train_dist.py --model_name=$name \
+                        --config=$configs \
+                        --resume=$resume \
+                        --log_interval=$log_interval \
+                        --valid_interval=$valid_interval \
+                        --use_gpu=$use_gpu \
+                        --save_dir=$save_dir \
+                        --fix_random_seed=$fix_random_seed
+    elif [ "$pretrain"x != ""x ]; then
+        python -m paddle.distributed.launch --log_dir=log \
+                train_dist.py --model_name=$name \
+                        --config=$configs \
+                        --pretrain=$pretrain \
+                        --log_interval=$log_interval \
+                        --valid_interval=$valid_interval \
+                        --use_gpu=$use_gpu \
+                        --save_dir=$save_dir \
+                        --fix_random_seed=$fix_random_seed
+    else
+        python -m paddle.distributed.launch --log_dir=log \
+                train_dist.py --model_name=$name \
+                        --config=$configs \
+                        --log_interval=$log_interval \
+                        --valid_interval=$valid_interval \
+                        --use_gpu=$use_gpu \
+                        --save_dir=$save_dir \
+                        --fix_random_seed=$fix_random_seed
+
+    fi
+else
+    echo "Not implemented mode " $mode
+fi
+

PaddleCV/video/train_dist.py

+#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+import os
+import sys
+import time
+import argparse
+import ast
+import logging
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+
+from utils.train_utils import train_with_dataloader
+import models
+from utils.config_utils import *
+from reader import get_reader
+from metrics import get_metrics
+from utils.utility import check_cuda
+from utils.utility import check_version
+
+logging.root.handlers = []
+FORMAT = '[%(levelname)s: %(filename)s: %(lineno)4d]: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT, stream=sys.stdout)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Paddle Video train script")
+    parser.add_argument(
+        '--model_name',
+        type=str,
+        default='AttentionCluster',
+        help='name of model to train.')
+    parser.add_argument(
+        '--config',
+        type=str,
+        default='configs/tsn_dist_and_dali.yaml',
+        help='path to config file of model')
+    parser.add_argument(
+        '--batch_size',
+        type=int,
+        default=None,
+        help='training batch size. None to use config file setting.')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=None,
+        help='learning rate use for training. None to use config file setting.')
+    parser.add_argument(
+        '--pretrain',
+        type=str,
+        default=None,
+        help='path to pretrain weights. None to use default weights path in  ~/.paddle/weights.'
+    )
+    parser.add_argument(
+        '--resume',
+        type=str,
+        default=None,
+        help='path to resume training based on previous checkpoints. '
+        'None for not resuming any checkpoints.')
+    parser.add_argument(
+        '--use_gpu',
+        type=ast.literal_eval,
+        default=True,
+        help='default use gpu.')
+    parser.add_argument(
+        '--no_memory_optimize',
+        action='store_true',
+        default=False,
+        help='whether to use memory optimize in train')
+    parser.add_argument(
+        '--epoch',
+        type=int,
+        default=None,
+        help='epoch number, 0 for read from config file')
+    parser.add_argument(
+        '--valid_interval',
+        type=int,
+        default=1,
+        help='validation epoch interval, 0 for no validation.')
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default=os.path.join('data', 'checkpoints'),
+        help='directory name to save train snapshoot')
+    parser.add_argument(
+        '--log_interval',
+        type=int,
+        default=10,
+        help='mini-batch interval to log.')
+    parser.add_argument(
+        '--fix_random_seed',
+        type=ast.literal_eval,
+        default=False,
+        help='If set True, enable continuous evaluation job.')
+    # NOTE: args for profiler, used for benchmark
+    parser.add_argument(
+        '--profiler_path',
+        type=str,
+        default='./',
+        help='the path to store profiler output file. used for benchmark.')
+    parser.add_argument(
+        '--is_profiler',
+        type=int,
+        default=0,
+        help='the switch profiler. used for benchmark.')
+    parser.add_argument(
+        '--num_trainers',
+        type=int,
+        default=1,
+        help='the number of trainers when used in distributed training. No need to set this, it will be set automatically'
+    )
+    parser.add_argument(
+        '--trainer_id',
+        type=int,
+        default=0,
+        help='trainer id when used in distributed training. No need to set this, it will be set automatically'
+    )
+    args = parser.parse_args()
+    return args
+
+
+def train(args):
+    # implement distributed training by fleet
+    use_fleet = True
+    if use_fleet:
+        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        fleet.init(role)
+        args.num_trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
+        args.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+        print('-------------', args.num_trainers, args.trainer_id)
+
+    if args.trainer_id == 0:
+        if not os.path.exists(args.save_dir):
+            os.makedirs(args.save_dir)
+
+    # parse config
+    config = parse_config(args.config)
+    train_config = merge_configs(config, 'train', vars(args))
+    print_configs(train_config, 'Train')
+    train_model = models.get_model(args.model_name, train_config, mode='train')
+
+    # build model
+    startup = fluid.Program()
+    train_prog = fluid.Program()
+    if args.fix_random_seed:
+        startup.random_seed = 1000
+        train_prog.random_seed = 1000
+    with fluid.program_guard(train_prog, startup):
+        with fluid.unique_name.guard():
+            train_model.build_input(use_dataloader=True)
+            train_model.build_model()
+            # for the input, has the form [data1, data2,..., label], so train_feeds[-1] is label
+            train_feeds = train_model.feeds()
+            train_fetch_list = train_model.fetches()
+            train_loss = train_fetch_list[0]
+            optimizer = train_model.optimizer()
+
+            if use_fleet:
+                optimizer = fleet.distributed_optimizer(optimizer)
+
+            optimizer.minimize(train_loss)
+            train_dataloader = train_model.dataloader()
+
+    gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
+    place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(startup)
+
+    if args.resume:
+        # if resume weights is given, load resume weights directly
+        assert os.path.exists(args.resume + '.pdparams'), \
+                "Given resume weight dir {}.pdparams not exist.".format(args.resume)
+        fluid.load(train_prog, model_path=args.resume, executor=exe)
+    else:
+        # if not in resume mode, load pretrain weights
+        if args.pretrain:
+            assert os.path.exists(args.pretrain), \
+                    "Given pretrain weight dir {} not exist.".format(args.pretrain)
+        pretrain = args.pretrain or train_model.get_pretrain_weights()
+        if pretrain:
+            train_model.load_pretrain_params(exe, pretrain, train_prog, place)
+
+    build_strategy = fluid.BuildStrategy()
+    build_strategy.enable_inplace = True
+    if args.model_name in ['CTCN']:
+        build_strategy.enable_sequential_execution = True
+
+    exec_strategy = fluid.ExecutionStrategy()
+
+    if use_fleet:
+        compiled_train_prog = fleet.main_program
+    else:
+        compiled_train_prog = fluid.compiler.CompiledProgram(
+            train_prog).with_data_parallel(
+                loss_name=train_loss.name,
+                build_strategy=build_strategy,
+                exec_strategy=exec_strategy)
+    # get reader
+    bs_denominator = 1
+    if args.use_gpu:
+        # check number of GPUs
+        gpus = os.getenv("CUDA_VISIBLE_DEVICES", "")
+        if gpus == "":
+            pass
+        else:
+            gpus = gpus.split(",")
+            num_gpus = len(gpus)
+            assert num_gpus == train_config.TRAIN.num_gpus, \
+                   "num_gpus({}) set by CUDA_VISIBLE_DEVICES " \
+                   "shoud be the same as that " \
+                   "set in {}({})".format(
+                   num_gpus, args.config, train_config.TRAIN.num_gpus)
+        bs_denominator = train_config.TRAIN.num_gpus
+
+    train_config.TRAIN.batch_size = int(train_config.TRAIN.batch_size /
+                                        bs_denominator)
+    train_reader = get_reader(args.model_name.upper(), 'train', train_config)
+
+    # get metrics 
+    train_metrics = get_metrics(args.model_name.upper(), 'train', train_config)
+
+    epochs = args.epoch or train_model.epoch_num()
+    exe_places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
+
+    train_dataloader.set_batch_generator(train_reader, places=place)
+
+    train_with_dataloader(
+        exe,
+        train_prog,
+        compiled_train_prog,
+        train_dataloader,
+        train_fetch_list,
+        train_metrics,
+        epochs=epochs,
+        log_interval=args.log_interval,
+        save_dir=args.save_dir,
+        num_trainers=args.num_trainers,
+        trainer_id=args.trainer_id,
+        save_model_name=args.model_name,
+        fix_random_seed=args.fix_random_seed,
+        is_profiler=args.is_profiler,
+        profiler_path=args.profiler_path)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    # check whether the installed paddle is compiled with GPU
+    check_cuda(args.use_gpu)
+    check_version()
+    logger.info(args)
+
+    train(args)

PaddleCV/video/utils/train_utils.py

@@ -75,6 +75,7 @@ def test_with_dataloader(exe,
 def train_with_dataloader(exe, train_prog, compiled_train_prog, train_dataloader, \
                         train_fetch_list, train_metrics, epochs = 10, \
                         log_interval = 0, valid_interval = 0, save_dir = './', \
+                        num_trainers = 1, trainer_id = 0, \
                         save_model_name = 'model', fix_random_seed = False, \
                         compiled_test_prog = None, test_dataloader = None, \
                         test_fetch_list = None, test_metrics = None, \
@@ -89,17 +90,21 @@ def train_with_dataloader(exe, train_prog, compiled_train_prog, train_dataloader
         train_iter = 0
         epoch_periods = []
 
+        cur_time = time.time()
         for data in train_dataloader():
-            cur_time = time.time()
             train_outs = exe.run(compiled_train_prog,
                                  fetch_list=train_fetch_list,
                                  feed=data)
             period = time.time() - cur_time
             epoch_periods.append(period)
+            timeStamp = time.time()
+            localTime = time.localtime(timeStamp)
+            strTime = time.strftime("%Y-%m-%d %H:%M:%S", localTime)
             if log_interval > 0 and (train_iter % log_interval == 0):
                 train_metrics.calculate_and_log_out(train_outs, \
-                        info = '[TRAIN] Epoch {}, iter {} '.format(epoch, train_iter))
+                        info = '[TRAIN {}] Epoch {}, iter {}, time {}, '.format(strTime, epoch, train_iter, period))
             train_iter += 1
+            cur_time = time.time()
 
             # NOTE: profiler tools, used for benchmark
             if is_profiler and epoch == 0 and train_iter == log_interval:
@@ -115,15 +120,18 @@ def train_with_dataloader(exe, train_prog, compiled_train_prog, train_dataloader
 
         logger.info('[TRAIN] Epoch {} training finished, average time: {}'.
                     format(epoch, np.mean(epoch_periods[1:])))
-        save_model(exe, train_prog, save_dir, save_model_name,
-                   "_epoch{}".format(epoch))
+
+        if trainer_id == 0:
+            save_model(exe, train_prog, save_dir, save_model_name,
+                       "_epoch{}".format(epoch))
         if compiled_test_prog and valid_interval > 0 and (
                 epoch + 1) % valid_interval == 0:
             test_with_dataloader(exe, compiled_test_prog, test_dataloader,
                                  test_fetch_list, test_metrics, log_interval,
                                  save_model_name)
 
-    save_model(exe, train_prog, save_dir, save_model_name)
+    if trainer_id == 0:
+        save_model(exe, train_prog, save_dir, save_model_name)
     #when fix_random seed for debug
     if fix_random_seed:
         cards = os.environ.get('CUDA_VISIBLE_DEVICES')

PaddleNLP/dialogue_domain_classification/README.MD

@@ -10,11 +10,11 @@
 
 
 ## 快速开始
-**目前模型要求使用PaddlePaddle 1.6及以上版本或适当的develop版本运行。**
+**目前模型要求使用PaddlePaddle 1.8及以上版本或适当的develop版本运行。**
 
 ### 1. Paddle版本安装
 
-本项目训练模块兼容Python2.7.x以及Python3.7.x， 依赖PaddlePaddle 1.6版本以及CentOS系统环境， 安装请参考官网 [快速安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/install/index_cn.html)。
+本项目训练模块兼容Python2.7.x以及Python3.7.x， 依赖PaddlePaddle 1.8版本以及CentOS系统环境， 安装请参考官网 [快速安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/install/index_cn.html)。
 
 注意：该模型同时支持cpu和gpu训练和预测，用户可以根据自身需求，选择安装对应的paddlepaddle-gpu或paddlepaddle版本。
 

PaddleNLP/dialogue_domain_classification/run_classifier.py

@@ -452,7 +452,7 @@ def main(args):
     if args.use_cuda:
         test_place = fluid.cuda_places(0)
         place = fluid.cuda_places()
-        DEV_COUNT = fluid.core.get_cuda_device_count()
+        DEV_COUNT = len(place)
     else:
         test_place = fluid.cpu_places(1)
         os.environ['CPU_NUM'] = str(args.cpu_num)

PaddleNLP/dialogue_domain_classification/utils.py

@@ -130,12 +130,11 @@ class DataReader(object):
         assert os.path.exists(data_path), "The given data file does not exist."
         if mode == "train":
             train_reader = fluid.io.batch(
-                paddle.reader.shuffle(
+                fluid.io.shuffle(
                     self.data_reader(
                         data_path, self.max_len, shuffle=True),
                     buf_size=batch_size * 100),
                 batch_size)
-            # train_reader = fluid.io.batch(self.data_reader(data_path), batch_size)                   
             return train_reader
         else:
             test_reader = fluid.io.batch(

PaddleNLP/dialogue_system/auto_dialogue_evaluation/README.md

@@ -30,7 +30,7 @@
 - cuda >= 9.0
 - cudnn >= 7.0
 - pandas >= 0.20.1
-- PaddlePaddle >= 1.7.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
+- PaddlePaddle >= 1.8.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
 
 ####   b、下载代码
 
@@ -119,13 +119,10 @@ emb_size: embedding层大小
 vocab_size: 词表大小
 sample_pro: 采样比率
 output_prediction_file: 输出的预测文件
-init_from_checkpoint: 加载断点模型
 init_from_params: 训练好的模型参数文件，一般用于预测
 init_from_pretrain_model: 预训练模型路径，如bert的模型参数
 inference_model_dir: inference model的保存路径
 save_model_path: 训练产出模型的输出路径
-save_checkpoint: 调用paddle的io接口save_persistables(把传入的层中所有参数以及优化器进行保存)来保存模型参数
-save_param: 调用paddle的io接口save_params(从main_program中取出所有参数然后保存到文件中)来保存模型参数
 evaluation_file: 参与评估的inference 文件
 vocab_path: 词表路径
 max_seq_len: 输入最大序列长度
@@ -199,7 +196,6 @@ python -u main.py \
       --loss_type="CLS" \
       --max_seq_len=50 \
       --save_model_path="data/saved_models/matching_pretrained" \
-      --save_param="params" \
       --training_file="data/input/data/unlabel_data/train.ids" \
       --epoch=20 \
       --print_step=1 \
@@ -217,7 +213,7 @@ python -u main.py \
 #### windows环境下：
 训练：
 ```
-python -u main.py --do_train=true --use_cuda=false --loss_type=CLS --max_seq_len=50 --save_model_path=data\saved_models\matching_pretrained  --save_param=params  --training_file=data\input\data\unlabel_data\train.ids --epoch=20 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
+python -u main.py --do_train=true --use_cuda=false --loss_type=CLS --max_seq_len=50 --save_model_path=data\saved_models\matching_pretrained --training_file=data\input\data\unlabel_data\train.ids --epoch=20 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
 ```
 
 #### 2、第二阶段finetuning模型的训练：
@@ -271,9 +267,8 @@ python -u main.py \
       --use_cuda=${use_cuda} \
       --loss_type="L2" \
       --max_seq_len=50 \
-      --init_from_pretrain_model="data/saved_models/trained_models/matching_pretrained/params" \
+      --init_from_pretrain_model="data/saved_models/trained_models/matching_pretrained/params/params" \
       --save_model_path="data/saved_models/human_finetuned" \
-      --save_param="params" \
       --training_file="data/input/data/label_data/human/train.ids" \
       --epoch=50 \
       --print_step=1 \
@@ -288,7 +283,7 @@ python -u main.py \
 
 #### windows环境下：
 ```
-python -u main.py --do_train=true --use_cuda=false --loss_type=L2 --max_seq_len=50 --save_model_path=data\saved_models\human_finetuned  --save_param=params  --training_file=data\input\data\label_data\human\train.ids --epoch=50 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
+python -u main.py --do_train=true --use_cuda=false --loss_type=L2 --max_seq_len=50 --save_model_path=data\saved_models\human_finetuned --training_file=data\input\data\label_data\human\train.ids --epoch=50 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
 ```
 
 ### 模型预测

PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/prepare_data_and_model.py

@@ -29,7 +29,7 @@ DATA_MODEL_PATH = {
     "DATA_PATH":
     "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz",
     "TRAINED_MODEL":
-    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"
+    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.3.0.0.tar.gz"
 }
 
 PATH_MAP = {'DATA_PATH': "./data/input", 'TRAINED_MODEL': './data/saved_models'}

PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade_net.py

@@ -34,7 +34,7 @@ def create_net(is_training,
     label = model_input.labels
 
     #emb
-    context_emb = fluid.input.embedding(
+    context_emb = fluid.embedding(
         input=context_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,
@@ -42,7 +42,7 @@ def create_net(is_training,
             name=word_emb_name,
             initializer=fluid.initializer.Normal(scale=0.1)))
 
-    response_emb = fluid.input.embedding(
+    response_emb = fluid.embedding(
         input=response_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,

PaddleNLP/dialogue_system/auto_dialogue_evaluation/data/config/ade.yaml

@@ -14,13 +14,10 @@ emb_size: 256
 vocab_size: 484016
 sample_pro: 1.0
 output_prediction_file: ""
-init_from_checkpoint: ""
 init_from_params: ""
 init_from_pretrain_model: ""
 inference_model_dir: ""
 save_model_path: ""
-save_checkpoint: ""
-save_param: ""
 evaluation_file: ""
 vocab_path: ""
 max_seq_len: 128

PaddleNLP/dialogue_system/auto_dialogue_evaluation/inference_model.py

@@ -27,7 +27,6 @@ from ade_net import create_net
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 
 def do_save_inference_model(args):
@@ -55,7 +54,7 @@ def do_save_inference_model(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
@@ -72,9 +71,9 @@ def do_save_inference_model(args):
     assert (args.init_from_params) or (args.init_from_pretrain_model)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
     elif args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_pretrain_model)
 
     # saving inference model
     fluid.io.save_inference_model(

PaddleNLP/dialogue_system/auto_dialogue_evaluation/predict.py

@@ -29,7 +29,6 @@ from ade_net import create_net
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 
 def do_predict(args):
@@ -59,12 +58,11 @@ def do_predict(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
                 is_training=False, model_input=input_field, args=args)
-            logits.persistable = True
 
             fetch_list = [logits.name]
     #for_test is True if change the is_test attribute of operators to True
@@ -79,9 +77,9 @@ def do_predict(args):
 
     assert (args.init_from_params) or (args.init_from_pretrain_model)
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params, executor=exe)
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_pretrain_model, executor=exe)
 
     compiled_test_prog = fluid.CompiledProgram(test_prog)
 
@@ -94,7 +92,7 @@ def do_predict(args):
         place=place, phase="test", shuffle=False, sample_pro=1)
     num_test_examples = processor.get_num_examples(phase='test')
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
     data_reader.start()
 
     scores = []
@@ -110,7 +108,7 @@ def do_predict(args):
     print("Write the predicted results into the output_prediction_file")
     fw = io.open(args.output_prediction_file, 'w', encoding="utf8")
     for index, score in enumerate(scores):
-        fw.write("%s\t%s\n" % (index, score))
+        fw.write(u"%s\t%s\n" % (index, score[0]))
     print("finish........................................")
 
 

PaddleNLP/dialogue_system/auto_dialogue_evaluation/run.sh

@@ -67,7 +67,6 @@ function pretrain_train()
       --loss_type="CLS" \
       --max_seq_len=50 \
       --save_model_path=${pretrain_model_path} \
-      --save_param="params" \
       --training_file="${INPUT_PATH}/unlabel_data/train.ids" \
       --epoch=20 \
       --print_step=1 \
@@ -99,9 +98,8 @@ function finetuning_train()
       --use_cuda=${1} \
       --loss_type="L2" \
       --max_seq_len=50 \
-      --init_from_pretrain_model="${SAVED_MODELS}/matching_pretrained/params/step_final" \
+      --init_from_pretrain_model="${SAVED_MODELS}/matching_pretrained/step_final" \
       --save_model_path=${save_model_path} \
-      --save_param="params" \
       --training_file="${INPUT_PATH}/label_data/${2}/train.ids" \
       --epoch=50 \
       --print_step=1 \
@@ -121,7 +119,7 @@ function pretrain_predict()
       --do_predict=true \
       --use_cuda=${1} \
       --predict_file="${INPUT_PATH}/unlabel_data/test.ids" \
-      --init_from_params="${SAVED_MODELS}/trained_models/matching_pretrained/params" \
+      --init_from_params="${SAVED_MODELS}/trained_models/matching_pretrained/params/params" \
       --loss_type="CLS" \
       --output_prediction_file="${OUTPUT_PATH}/pretrain_matching_predict" \
       --max_seq_len=50 \
@@ -137,7 +135,7 @@ function finetuning_predict()
       --do_predict=true \
       --use_cuda=${1} \
       --predict_file="${INPUT_PATH}/label_data/${2}/test.ids" \
-      --init_from_params=${SAVED_MODELS}/trained_models/${2}_finetuned/params \
+      --init_from_params="${SAVED_MODELS}/trained_models/${2}_finetuned/params/params" \
       --loss_type="L2" \
       --output_prediction_file="${OUTPUT_PATH}/finetuning_${2}_predict" \
       --max_seq_len=50 \

PaddleNLP/dialogue_system/auto_dialogue_evaluation/train.py

@@ -29,7 +29,6 @@ from ade_net import create_net, set_word_embedding
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 try:
     import cPickle as pickle  #python 2
@@ -62,24 +61,27 @@ def do_train(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             loss = create_net(
                 is_training=True, model_input=input_field, args=args)
-            loss.persistable = True
+
             # gradient clipping
-            fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
-                max=1.0, min=-1.0))
-            optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+            optimizer = fluid.optimizer.AdamOptimizer(
+                learning_rate=args.learning_rate,
+                grad_clip=fluid.clip.GradientClipByValue(
+                    max=1.0, min=-1.0))
             optimizer.minimize(loss)
 
             if args.use_cuda:
-                dev_count = fluid.core.get_cuda_device_count()
+                places = fluid.cuda_places()
+                dev_count = len(places)
                 place = fluid.CUDAPlace(
                     int(os.getenv('FLAGS_selected_gpus', '0')))
             else:
-                dev_count = int(os.environ.get('CPU_NUM', 1))
+                places = fluid.cpu_places()
+                dev_count = len(places)
                 place = fluid.CPUPlace()
 
             processor = reader.DataProcessor(
@@ -99,20 +101,20 @@ def do_train(args):
             print("Num train examples: %d" % num_train_examples)
             print("Max train steps: %d" % max_train_steps)
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
 
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_checkpoint == "") or (
+    assert (args.init_from_params == "") or (
         args.init_from_pretrain_model == "")
 
     #init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint:
-        save_load_io.init_from_checkpoint(args, exe, train_prog)
+    if args.init_from_params:
+        fluid.load(train_prog, args.init_from_params, exe)
     #init from some pretrain models, to better solve the current task
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, train_prog)
+        fluid.load(train_prog, args.init_from_pretrain_model, exe)
 
     if args.word_emb_init:
         print("start loading word embedding init ...")
@@ -163,21 +165,17 @@ def do_train(args):
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
-                    if args.save_checkpoint:
-                        save_load_io.save_checkpoint(args, exe, train_prog,
-                                                     "step_" + str(steps))
-                    if args.save_param:
-                        save_load_io.save_param(args, exe, train_prog,
-                                                "step_" + str(steps))
+                    model_path = os.path.join(args.save_model_path,
+                                              "step_" + str(steps))
+                    fluid.save(train_prog, model_path)
+
                 steps += 1
             except fluid.core.EOFException:
                 data_reader.reset()
                 break
 
-    if args.save_checkpoint:
-        save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
-    if args.save_param:
-        save_load_io.save_param(args, exe, train_prog, "step_final")
+    model_path = os.path.join(args.save_model_path, "step_final")
+    fluid.save(train_prog, model_path)
 
     def get_cards():
         num = 0

PaddleNLP/dialogue_system/dialogue_general_understanding/README.md

@@ -23,7 +23,7 @@
 - Python >= 2.7
 - cuda >= 9.0
 - cudnn >= 7.0
-- PaddlePaddle >= 1.7.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
+- PaddlePaddle >= 1.8.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
 
 ####   b、下载代码
 
@@ -123,13 +123,10 @@ format：conversation_content \t question \1 answer \t state1 state2 state3.....
 task_name: 任务名称，可选udc、swda、mrda、atis_intent、atis_slot、dstc2
 data_dir: 数据路径，如./data/input/data/udc
 bert_config_path: 预训练模型bert的网络配置./data/pretrain_model/uncased_L-12_H-768_A-12/bert_config.json
-init_from_checkpoint: 加载断点模型
 init_from_params: 训练好的模型参数文件，一般用于预测
 init_from_pretrain_model: 预训练模型路径，如bert的模型参数
 inference_model_dir: inference model的保存路径
 save_model_path: 训练产出模型的输出路径
-save_checkpoint: 调用paddle的io接口save_persistables(把传入的层中所有参数以及优化器进行保存)来保存模型参数
-save_param: 调用paddle的io接口save_params(从main_program中取出所有参数然后保存到文件中)来保存模型参数
 lr_scheduler: learning rate scheduler
 weight_decay: learning rate 权重衰减因子
 warmup_proportion: warmup比率
@@ -221,7 +218,6 @@ python -u main.py \
        --vocab_path="${BERT_BASE_PATH}/vocab.txt" \
        --init_from_pretrain_model="${BERT_BASE_PATH}/params" \
        --save_model_path="./data/saved_models/${TASK_NAME}" \
-       --save_param="params" \
        --save_steps=100 \
        --learning_rate=2e-5 \
        --weight_decay=0.01 \
@@ -235,7 +231,7 @@ python -u main.py \
 
 #### windows环境下
 ```
-python -u main.py --task_name=atis_intent --use_cuda=false --do_train=true --epoch=20 --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --init_from_pretrain_model=data\pretrain_model\uncased_L-12_H-768_A-12\params --save_model_path=data\saved_models\atis_intent --save_param=params --save_steps=100 --learning_rate=2e-5 --weight_decay=0.01 --max_seq_len=128 --print_steps=10
+python -u main.py --task_name=atis_intent --use_cuda=false --do_train=true --epoch=20 --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --init_from_pretrain_model=data\pretrain_model\uncased_L-12_H-768_A-12\params --save_model_path=data\saved_models\atis_intent --save_steps=100 --learning_rate=2e-5 --weight_decay=0.01 --max_seq_len=128 --print_steps=10
 ```
 
 ### 模型预测
@@ -294,7 +290,7 @@ python -u main.py \
        --batch_size=32 \
        --do_lower_case=true \
        --data_dir="./data/input/data/atis/${TASK_NAME}" \
-       --init_from_params="./data/saved_models/trained_models/${TASK_NAME}/params" \
+       --init_from_params="./data/saved_models/trained_models/${TASK_NAME}/params/params" \
        --bert_config_path="${BERT_BASE_PATH}/bert_config.json" \
        --vocab_path="${BERT_BASE_PATH}/vocab.txt" \
        --output_prediction_file="./data/output/pred_${TASK_NAME}" \
@@ -305,7 +301,7 @@ python -u main.py \
 
 #### windows环境下
 ```
-python -u main.py --task_name=atis_intent --use_cuda=false --do_predict=true --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --init_from_params=data\saved_models\trained_models\atis_intent\params --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --output_prediction_file=data\output\pred_atis_intent --max_seq_len=128
+python -u main.py --task_name=atis_intent --use_cuda=false --do_predict=true --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --init_from_params=data\saved_models\trained_models\atis_intent\params\params --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --output_prediction_file=data\output\pred_atis_intent --max_seq_len=128
 ```
 
 ### 模型评估

PaddleNLP/dialogue_system/dialogue_general_understanding/data/config/dgu.yaml

 task_name: ""
 data_dir: ""
 bert_config_path: ""
-init_from_checkpoint: ""
 init_from_params: ""
 init_from_pretrain_model: ""
 inference_model_dir: ""
 save_model_path: ""
-save_checkpoint: ""
-save_param: ""
 lr_scheduler: "linear_warmup_decay"
 weight_decay: 0.01
 warmup_proportion: 0.1

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/bert.py

@@ -87,21 +87,21 @@ class BertModel(object):
 
     def _build_model(self, src_ids, position_ids, sentence_ids, input_mask):
         # padding id in vocabulary must be set to 0
-        emb_out = fluid.input.embedding(
+        emb_out = fluid.embedding(
             input=src_ids,
             size=[self._voc_size, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._word_emb_name, initializer=self._param_initializer),
             is_sparse=False)
-        position_emb_out = fluid.input.embedding(
+        position_emb_out = fluid.embedding(
             input=position_ids,
             size=[self._max_position_seq_len, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._pos_emb_name, initializer=self._param_initializer))
 
-        sent_emb_out = fluid.input.embedding(
+        sent_emb_out = fluid.embedding(
             sentence_ids,
             size=[self._sent_types, self._emb_size],
             dtype=self._dtype,

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_paradigm.py

@@ -48,8 +48,8 @@ class Paradigm(object):
                 initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
             bias_attr=fluid.ParamAttr(
                 name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
-
-        if not params['is_training']:
+        
+        if not params['is_training']: 
             probs = fluid.layers.softmax(logits)
             results = {"probs": probs}
             return results

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_predict_pack.py

@@ -17,7 +17,6 @@ import re
 import sys
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/optimization.py

@@ -59,7 +59,13 @@ def optimization(loss,
                  weight_decay,
                  scheduler='linear_warmup_decay',
                  use_fp16=False,
-                 loss_scaling=1.0):
+                 loss_scaling=1.0,
+                 clip_norm_thres=1.0):
+    # When using mixed precision training, scale the gradient clip threshold
+    # by loss_scaling
+    if use_fp16 and loss_scaling > 1.0:
+        clip_norm_thres *= loss_scaling
+
     if warmup_steps > 0:
         if scheduler == 'noam_decay':
             scheduled_lr = fluid.layers.learning_rate_scheduler\
@@ -71,19 +77,17 @@ def optimization(loss,
         else:
             raise ValueError("Unkown learning rate scheduler, should be "
                              "'noam_decay' or 'linear_warmup_decay'")
-        optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
+        optimizer = fluid.optimizer.AdamOptimizer(
+            learning_rate=scheduled_lr,
+            grad_clip=fluid.clip.GradientClipByGlobalNorm(
+                clip_norm=clip_norm_thres))
     else:
-        optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
+        optimizer = fluid.optimizer.AdamOptimizer(
+            learning_rate=learning_rate,
+            grad_clip=fluid.clip.GradientClipByGlobalNorm(
+                clip_norm=clip_norm_thres))
         scheduled_lr = learning_rate
 
-    clip_norm_thres = 1.0
-    # When using mixed precision training, scale the gradient clip threshold
-    # by loss_scaling
-    if use_fp16 and loss_scaling > 1.0:
-        clip_norm_thres *= loss_scaling
-    fluid.clip.set_gradient_clip(
-        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres))
-
     def exclude_from_weight_decay(name):
         if name.find("layer_norm") > -1:
             return True

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/prepare_data_and_model.py

@@ -29,7 +29,7 @@ DATA_MODEL_PATH = {
     "DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz",
     "PRETRAIN_MODEL":
     "https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz",
-    "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"
+    "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_3.0.0.tar.gz"
 }
 
 PATH_MAP = {

PaddleNLP/dialogue_system/dialogue_general_understanding/inference_model.py

@@ -25,7 +25,6 @@ import paddle.fluid as fluid
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 
 import dgu.reader as reader
 from dgu_net import create_net
@@ -97,12 +96,10 @@ def do_save_inference_model(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_params) or (args.init_from_pretrain_model)
+    assert (args.init_from_params)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
-    elif args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
 
     # saving inference model
     fluid.io.save_inference_model(

PaddleNLP/dialogue_system/dialogue_general_understanding/main.py

@@ -16,7 +16,6 @@ import os
 import sys
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 from eval import do_eval

PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py

@@ -19,7 +19,6 @@ import sys
 import numpy as np
 import argparse
 import collections
-import paddle
 import paddle.fluid as fluid
 
 import dgu.reader as reader
@@ -30,7 +29,6 @@ import dgu.define_predict_pack as define_predict_pack
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 from dgu.utils.py23 import tab_tok, rt_tok
 
 
@@ -84,7 +82,7 @@ def do_predict(args):
 
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             results = create_net(
@@ -95,9 +93,6 @@ def do_predict(args):
                 args=args)
 
             probs = results.get("probs", None)
-
-            probs.persistable = True
-
             fetch_list = [probs.name]
 
     #for_test is True if change the is_test attribute of operators to True
@@ -111,12 +106,10 @@ def do_predict(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_params) or (args.init_from_pretrain_model)
+    assert (args.init_from_params)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
-    if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
 
     compiled_test_prog = fluid.CompiledProgram(test_prog)
 
@@ -130,7 +123,7 @@ def do_predict(args):
     batch_generator = processor.data_generator(
         batch_size=args.batch_size, phase='test', shuffle=False)
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
     data_reader.start()
 
     all_results = []

PaddleNLP/dialogue_system/dialogue_general_understanding/run.sh

@@ -3,7 +3,7 @@
 export FLAGS_sync_nccl_allreduce=0
 export FLAGS_eager_delete_tensor_gb=1
 
-export CUDA_VISIBLE_DEVICES=1
+export CUDA_VISIBLE_DEVICES=
 if  [ ! "$CUDA_VISIBLE_DEVICES" ]
 then
     export CPU_NUM=1
@@ -21,7 +21,7 @@ SAVE_MODEL_PATH="./data/saved_models/${TASK_NAME}"
 TRAIN_MODEL_PATH="./data/saved_models/trained_models"
 OUTPUT_PATH="./data/output"
 INFERENCE_MODEL="data/inference_models"
-PYTHON_PATH="python3"
+PYTHON_PATH="python"
 
 if [ -f ${SAVE_MODEL_PATH} ]; then
     rm ${SAVE_MODEL_PATH}
@@ -94,7 +94,6 @@ else
   exit 255
 fi
 
-
 #training
 function train()
 {
@@ -110,7 +109,6 @@ function train()
        --vocab_path=${BERT_BASE_PATH}/vocab.txt \
        --init_from_pretrain_model=${BERT_BASE_PATH}/params \
        --save_model_path=${SAVE_MODEL_PATH} \
-       --save_param="params" \
        --save_steps=${save_steps} \
        --learning_rate=${learning_rate} \
        --weight_decay=0.01 \
@@ -128,7 +126,7 @@ function predict()
        --batch_size=${batch_size} \
        --data_dir=${INPUT_PATH} \
        --do_lower_case=true \
-       --init_from_params=${TRAIN_MODEL_PATH}/${TASK_NAME}/params \
+       --init_from_params=${TRAIN_MODEL_PATH}/${TASK_NAME}/params/params \
        --bert_config_path=${BERT_BASE_PATH}/bert_config.json \
        --vocab_path=${BERT_BASE_PATH}/vocab.txt \
        --output_prediction_file=${OUTPUT_PATH}/pred_${TASK_NAME} \

PaddleNLP/dialogue_system/dialogue_general_understanding/train.py

@@ -22,7 +22,6 @@ import sys
 import time
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 from dgu_net import create_net
@@ -32,7 +31,6 @@ import dgu.define_paradigm as define_paradigm
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 
 
 def do_train(args):
@@ -80,8 +78,9 @@ def do_train(args):
 
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            
-            data_reader = fluid.io.DataLoader.from_generator(feed_list=input_inst, capacity=4, iterable=False)
+
+            data_reader = fluid.io.DataLoader.from_generator(
+                feed_list=input_inst, capacity=4, iterable=False)
 
             processor = processors[task_name](data_dir=args.data_dir,
                                               vocab_path=args.vocab_path,
@@ -103,13 +102,8 @@ def do_train(args):
             accuracy = results.get("accuracy", None)
             num_seqs = results.get("num_seqs", None)
 
-            loss.persistable = True
-            probs.persistable = True
-            if accuracy:
-                accuracy.persistable = True
-            num_seqs.persistable = True
-
-            places = fluid.cuda_places() if args.use_cuda else fluid.cpu_places()
+            places = fluid.cuda_places() if args.use_cuda else fluid.cpu_places(
+            )
             dev_count = len(places)
 
             batch_generator = processor.data_generator(
@@ -149,16 +143,13 @@ def do_train(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_checkpoint == "") or (
-        args.init_from_pretrain_model == "")
+    assert args.init_from_params or args.init_from_pretrain_model
 
     # init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint:
-        save_load_io.init_from_checkpoint(args, exe, train_prog)
-
-    # init from some pretrain models, to better solve the current task
+    if args.init_from_params:
+        fluid.load(train_prog, args.init_from_params, exe)
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, train_prog)
+        fluid.load(train_prog, args.init_from_pretrain_model, exe)
 
     build_strategy = fluid.compiler.BuildStrategy()
     build_strategy.enable_inplace = True
@@ -234,21 +225,16 @@ def do_train(args):
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
-                    save_path = "step_" + str(steps)
-                    if args.save_checkpoint:
-                        save_load_io.save_checkpoint(args, exe, train_prog,
-                                                     save_path)
-                    if args.save_param:
-                        save_load_io.save_param(args, exe, train_prog,
-                                                save_path)
+                    model_path = os.path.join(args.save_model_path,
+                                              "step_" + str(steps))
+                    fluid.save(train_prog, model_path)
 
             except fluid.core.EOFException:
                 data_reader.reset()
                 break
-    if args.save_checkpoint:
-        save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
-    if args.save_param:
-        save_load_io.save_param(args, exe, train_prog, "step_final")
+
+    model_path = os.path.join(args.save_model_path, "step_final")
+    fluid.save(train_prog, model_path)
 
     def get_cards():
         num = 0

PaddleNLP/lexical_analysis/creator.py

@@ -105,15 +105,15 @@ def create_pyreader(args,
         # create lac pyreader
         if mode == 'train':
             pyreader.set_sample_list_generator(
-                fluid.io.batch(
-                    fluid.io.shuffle(
+                paddle.batch(
+                    paddle.reader.shuffle(
                         reader.file_reader(file_name),
                         buf_size=args.traindata_shuffle_buffer),
                     batch_size=args.batch_size / device_count),
                 places=place)
         else:
             pyreader.set_sample_list_generator(
-                fluid.io.batch(
+                paddle.batch(
                     reader.file_reader(
                         file_name, mode=mode),
                     batch_size=args.batch_size / device_count),

PaddleNLP/lexical_analysis/run_ernie_sequence_labeling.py

@@ -116,11 +116,11 @@ def do_train(args):
                 feed_list=train_ret['feed_list'],
                 model="ernie",
                 place=place)
-
+            
+            clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0)
             optimizer = fluid.optimizer.Adam(
-                learning_rate=args.base_learning_rate)
-            fluid.clip.set_gradient_clip(
-                clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0))
+                learning_rate=args.base_learning_rate, 
+                grad_clip=clip)
             optimizer.minimize(train_ret["avg_cost"])
 
     lower_mem, upper_mem, unit = fluid.contrib.memory_usage(

PaddleNLP/seq2seq/seq2seq/train.py

@@ -97,14 +97,14 @@ def main():
                 dropout=dropout)
         loss = model.build_graph()
         inference_program = train_program.clone(for_test=True)
-        fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(
-            clip_norm=max_grad_norm))
+        clip=fluid.clip.GradientClipByGlobalNorm(
+            clip_norm=max_grad_norm)
         lr = args.learning_rate
         opt_type = args.optimizer
         if opt_type == "sgd":
-            optimizer = fluid.optimizer.SGD(lr)
+            optimizer = fluid.optimizer.SGD(lr, grad_clip=clip)
         elif opt_type == "adam":
-            optimizer = fluid.optimizer.Adam(lr)
+            optimizer = fluid.optimizer.Adam(lr, grad_clip=clip)
         else:
             print("only support [sgd|adam]")
             raise Exception("opt type not support")

PaddleNLP/seq2seq/variational_seq2seq/model.py

@@ -229,7 +229,7 @@ class VAE(object):
 
         # `sample_output_layer` samples an id from the logits distribution instead of argmax(logits)
         # it will be used within BeamSearchDecoder
-        sample_output_layer = lambda x: layers.unsqueeze(layers.one_hot(
+        sample_output_layer = lambda x: layers.unsqueeze(fluid.one_hot(
             layers.unsqueeze(
             layers.sampling_id(
                 layers.softmax(

PaddleNLP/seq2seq/variational_seq2seq/train.py

@@ -89,9 +89,8 @@ def main():
             inference_program = fluid.default_main_program().clone(
                 for_test=True)
 
-            fluid.clip.set_gradient_clip(
-                clip=fluid.clip.GradientClipByGlobalNorm(
-                    clip_norm=max_grad_norm))
+            clip=fluid.clip.GradientClipByGlobalNorm(
+                    clip_norm=max_grad_norm)
 
             learning_rate = fluid.layers.create_global_var(
                 name="learning_rate",
@@ -102,9 +101,9 @@ def main():
 
             opt_type = args.optimizer
             if opt_type == "sgd":
-                optimizer = fluid.optimizer.SGD(learning_rate)
+                optimizer = fluid.optimizer.SGD(learning_rate, grad_clip=clip)
             elif opt_type == "adam":
-                optimizer = fluid.optimizer.Adam(learning_rate)
+                optimizer = fluid.optimizer.Adam(learning_rate, grad_clip=clip)
             else:
                 print("only support [sgd|adam]")
                 raise Exception("opt type not support")
@@ -272,7 +271,7 @@ def main():
                           (old_lr, new_lr))
 
                     dir_name = args.model_path + "/epoch_" + str(best_epoch_id)
-                    fluid.io.load_params(exe, dir_name)
+                    fluid.load(main_program, dir_name, exe)
 
                     decay_cnt += 1
                     if decay_cnt == max_decay:

PaddleRec/gru4rec/dy_graph/gru4rec_dy.py

@@ -247,31 +247,7 @@ def train_ptb_lm():
     model_type = args.model_type
 
     vocab_size = 37484
-    if model_type == "test":
-        num_layers = 1
-        batch_size = 2
-        hidden_size = 10
-        num_steps = 4
-        init_scale = 0.1
-        max_grad_norm = 5.0
-        epoch_start_decay = 1
-        max_epoch = 1
-        dropout = 0.0
-        lr_decay = 0.5
-        base_learning_rate = 1.0
-    elif model_type == "small":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 200
-        num_steps = 20
-        init_scale = 0.1
-        max_grad_norm = 5.0
-        epoch_start_decay = 4
-        max_epoch = 2
-        dropout = 0.0
-        lr_decay = 0.5
-        base_learning_rate = 1.0
-    elif model_type == "gru4rec":
+    if model_type == "gru4rec":
         num_layers = 1
         batch_size = 500
         hidden_size = 100
@@ -283,30 +259,6 @@ def train_ptb_lm():
         dropout = 0.0
         lr_decay = 0.5
         base_learning_rate = 0.05
-    elif model_type == "medium":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 650
-        num_steps = 35
-        init_scale = 0.05
-        max_grad_norm = 5.0
-        epoch_start_decay = 6
-        max_epoch = 39
-        dropout = 0.5
-        lr_decay = 0.8
-        base_learning_rate = 1.0
-    elif model_type == "large":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 1500
-        num_steps = 35
-        init_scale = 0.04
-        max_grad_norm = 10.0
-        epoch_start_decay = 14
-        max_epoch = 55
-        dropout = 0.65
-        lr_decay = 1.0 / 1.15
-        base_learning_rate = 1.0
     else:
         print("model type not support")
         return
@@ -433,7 +385,7 @@ def train_ptb_lm():
                 out_loss = dy_loss.numpy()
                 acc_ = acc.numpy()[0]
 
-                init_hidden = last_hidden
+                init_hidden = last_hidden.detach()
                 dy_loss.backward()
                 sgd.minimize(dy_loss)
                 ptb_model.clear_gradients()

dygraph/cycle_gan/infer.py

@@ -50,7 +50,7 @@ def infer():
         out_path = args.output + "/single"
         if not os.path.exists(out_path):
             os.makedirs(out_path)
-        cycle_gan = Cycle_Gan("cycle_gan")
+        cycle_gan = Cycle_Gan(3)
         save_dir = args.init_model 
         restore, _ = fluid.load_dygraph(save_dir)
         cycle_gan.set_dict(restore)

dygraph/cycle_gan/test.py

@@ -50,7 +50,7 @@ def test():
         out_path = args.output + "/eval" + "/" + str(epoch)
         if not os.path.exists(out_path):
             os.makedirs(out_path)
-        cycle_gan = Cycle_Gan("cycle_gan")
+        cycle_gan = Cycle_Gan(3)
         save_dir = args.init_model + str(epoch)
         restore, _ = fluid.load_dygraph(save_dir)
         cycle_gan.set_dict(restore)

dygraph/cycle_gan/train.py

@@ -44,7 +44,7 @@ add_arg('save_checkpoints',  bool,  True,       "Whether to save checkpoints.")
 lambda_A = 10.0
 lambda_B = 10.0
 lambda_identity = 0.5
-tep_per_epoch = 2974
+step_per_epoch = 2974
 
 
 def optimizer_setting(parameters):
@@ -90,7 +90,8 @@ def train(args):
         losses = [[], []]
         t_time = 0
 
-        vars_G = cycle_gan.build_generator_resnet_9blocks_a.parameters() + cycle_gan.build_generator_resnet_9blocks_b.parameters()
+        vars_G = cycle_gan.build_generator_resnet_9blocks_a.parameters(
+        ) + cycle_gan.build_generator_resnet_9blocks_b.parameters()
         vars_da = cycle_gan.build_gen_discriminator_a.parameters()
         vars_db = cycle_gan.build_gen_discriminator_b.parameters()
 

dygraph/mobilenet/train.py

@@ -128,6 +128,7 @@ def train_mobilenet():
         test_data_loader.set_sample_list_generator(test_reader, place)
 
         # 4. train loop
+        total_batch_num = 0  #this is for benchmark
         for eop in range(args.num_epochs):
             if num_trainers > 1:
                 imagenet_reader.set_shuffle_seed(eop + (
@@ -142,6 +143,8 @@ def train_mobilenet():
             # 4.1 for each batch, call net() , backward(), and minimize()
             for img, label in train_data_loader():
                 t1 = time.time()
+                if args.max_iter and total_batch_num == args.max_iter:
+                    return
                 label = to_variable(label.numpy().astype('int64').reshape(
                     int(args.batch_size // place_num), 1))
                 t_start = time.time()
@@ -185,6 +188,10 @@ def train_mobilenet():
                 total_sample += 1
                 batch_id += 1
                 t_last = time.time()
+               
+                # NOTE: used for benchmark
+                total_batch_num = total_batch_num + 1
+
             if args.ce:
                 print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
                 print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))

dygraph/mobilenet/utils/utility.py

@@ -117,6 +117,10 @@ def parse_args():
     add_arg('drop_connect_rate',        float,  0.2,                    "The value of drop connect rate")
     parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
 
+    # NOTE: used for benchmark
+    add_arg('max_iter',                 int,    0,                      "The number of total train max_iters.")
+
+
     # READER AND PREPROCESS
     add_arg('lower_scale',              float,  0.08,                   "The value of lower_scale in ramdom_crop")
     add_arg('lower_ratio',              float,  3./4.,                  "The value of lower_ratio in ramdom_crop")