Add option for CE of pruning (#942)

demo/dygraph/pruning/train.py

@@ -7,6 +7,7 @@ import paddle
 import argparse
 import functools
 import math
+import random
 import time
 import numpy as np
 sys.path.append(
@@ -43,6 +44,7 @@ add_arg('pruned_ratio',     float, None,         "The ratios to be pruned.")
 add_arg('criterion',        str, "l1_norm",         "The prune criterion to be used, support l1_norm and batch_norm_scale.")
 add_arg('use_gpu',   bool, True,                "Whether to GPUs.")
 add_arg('checkpoint',   str, None,                "The path of checkpoint which is used for resume training.")
+add_arg('ce_test',          bool, False, "Whether to CE test.")
 # yapf: enable
 
 model_list = models.__all__
@@ -109,6 +111,16 @@ def create_optimizer(args, parameters, steps_per_epoch):
 
 
 def compress(args):
+    num_workers = 4
+    shuffle = True
+    if args.ce_test:
+        # set seed
+        seed = 111
+        paddle.seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+        num_workers = 0
+        shuffle = False
 
     paddle.set_device('gpu' if args.use_gpu else 'cpu')
     train_reader = None
@@ -187,7 +199,8 @@ def compress(args):
               batch_size=args.batch_size // ParallelEnv().nranks,
               verbose=1,
               save_dir=args.model_path,
-              num_workers=8)
+              num_workers=num_workers,
+              shuffle=shuffle)
 
 
 def main():

demo/prune/train.py

@@ -5,6 +5,7 @@ import paddle
 import argparse
 import functools
 import math
+import random
 import time
 import numpy as np
 sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir)
@@ -31,13 +32,14 @@ add_arg('momentum_rate',    float,  0.9,               "The value of momentum_ra
 add_arg('num_epochs',       int,  120,               "The number of total epochs.")
 parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
 add_arg('config_file',      str, None,                 "The config file for compression with yaml format.")
-add_arg('data',             str, "mnist",                 "Which data to use. 'mnist' or 'imagenet'")
+add_arg('data',             str, "cifar10",                 "Which data to use. 'cifar10' or 'imagenet'")
 add_arg('log_period',       int, 10,                 "Log period in batches.")
 add_arg('test_period',      int, 10,                 "Test period in epoches.")
 add_arg('model_path',       str, "./models",         "The path to save model.")
 add_arg('pruned_ratio',     float, None,         "The ratios to be pruned.")
 add_arg('criterion',        str, "l1_norm",         "The prune criterion to be used, support l1_norm and batch_norm_scale.")
 add_arg('save_inference',   bool, False,                "Whether to save inference model.")
+add_arg('ce_test',          bool, False, "Whether to CE test.")
 # yapf: enable
 
 model_list = models.__all__
@@ -94,16 +96,31 @@ def create_optimizer(args, step_per_epoch):
 
 
 def compress(args):
+
+    num_workers = 4
+    shuffle = True
+    if args.ce_test:
+        # set seed
+        seed = 111
+        paddle.seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+        num_workers = 0
+        shuffle = False
+
     train_reader = None
     test_reader = None
-    if args.data == "mnist":
+
+    need_pretrain = True
+    if args.data == "cifar10":
         transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
-        train_dataset = paddle.vision.datasets.MNIST(
-            mode='train', backend="cv2", transform=transform)
-        val_dataset = paddle.vision.datasets.MNIST(
-            mode='test', backend="cv2", transform=transform)
+        train_dataset = paddle.vision.datasets.Cifar10(
+            mode="train", backend="cv2", transform=transform)
+        val_dataset = paddle.vision.datasets.Cifar10(
+            mode="test", backend="cv2", transform=transform)
         class_dim = 10
-        image_shape = "1,28,28"
+        image_shape = "3, 32, 32"
+        need_pretrain = False
     elif args.data == "imagenet":
         import imagenet_reader as reader
         train_dataset = reader.ImageNetDataset(mode='train')
@@ -129,10 +146,10 @@ def compress(args):
         feed_list=[image, label],
         drop_last=True,
         batch_size=batch_size_per_card,
-        shuffle=True,
+        shuffle=shuffle,
         return_list=False,
         use_shared_memory=True,
-        num_workers=16)
+        num_workers=num_workers)
     valid_loader = paddle.io.DataLoader(
         val_dataset,
         places=place,
@@ -147,6 +164,7 @@ def compress(args):
     # model definition
     model = models.__dict__[args.model]()
     out = model.net(input=image, class_dim=class_dim)
+    label = paddle.reshape(label, [-1, 1])
     cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
     avg_cost = paddle.mean(x=cost)
     acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
@@ -157,7 +175,7 @@ def compress(args):
 
     exe.run(paddle.static.default_startup_program())
 
-    if args.pretrained_model:
+    if need_pretrain and args.pretrained_model:
 
         def if_exist(var):
             return os.path.exists(os.path.join(args.pretrained_model, var.name))