[Cherry-Pick]fix dygraph quant demo dataset issue (#556)

* fix dygraph quant demo dataset issue

* fix dygraph quant demo dataset issue

demo/dygraph/quant/README.md

@@ -22,7 +22,7 @@
 
 ### 配置量化参数
 
-```
+```python
 quant_config = {
     'weight_preprocess_type': None,
     'activation_preprocess_type': None,
@@ -70,9 +70,9 @@ quanter.save_quantized_model(net, 'save_dir', input_spec=[paddle.static.InputSpe
 
    ```bash
   # 单卡训练
-  python train.py --model='mobilenet_v1'
+  python train.py --model=mobilenet_v1
   # 多卡训练，以0到3号卡为例
-  python -m paddle.distributed.launch --gpus="0,1,2,3" train.py --model='mobilenet_v1'
+  python -m paddle.distributed.launch --gpus="0,1,2,3" train.py --model=mobilenet_v1
    ```
 - MobileNetV3
 

demo/dygraph/quant/train.py

@@ -28,6 +28,7 @@ import numpy as np
 from paddle.distributed import ParallelEnv
 from paddle.static import load_program_state
 from paddle.vision.models import mobilenet_v1
+import paddle.vision.transforms as T
 from paddleslim.common import get_logger
 from paddleslim.dygraph.quant import QAT
 
@@ -55,7 +56,7 @@ add_arg('use_pact',                 bool,   False,
 add_arg('momentum_rate',            float,  0.9,                                         "The value of momentum_rate.")
 add_arg('num_epochs',               int,    1,                                           "The number of total epochs.")
 add_arg('total_images',             int,    1281167,                                     "The number of total training images.")
-add_arg('data',                     str,    "imagenet",                                  "Which data to use. 'mnist' or 'imagenet'")
+add_arg('data',                     str,    "imagenet",                                  "Which data to use. 'cifar10' or 'imagenet'")
 add_arg('log_period',               int,    10,                                          "Log period in batches.")
 add_arg('model_save_dir',           str,    "./output_models",                           "model save directory.")
 parser.add_argument('--step_epochs', nargs='+', type=int, default=[10, 20, 30], help="piecewise decay step")
@@ -87,12 +88,16 @@ def load_dygraph_pretrain(model, path=None, load_static_weights=False):
 
 
 def compress(args):
-    if args.data == "mnist":
-        train_dataset = paddle.vision.datasets.MNIST(mode='train')
-        val_dataset = paddle.vision.datasets.MNIST(mode='test')
+    if args.data == "cifar10":
+        transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
+        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"
-        args.total_images = 60000
+        image_shape = [3, 32, 32]
+        pretrain = False
+        args.total_images = 50000
     elif args.data == "imagenet":
         import imagenet_reader as reader
         train_dataset = reader.ImageNetDataset(mode='train')
@@ -199,6 +204,8 @@ def compress(args):
             eval_reader_cost += time.time() - reader_start
             image = data[0]
             label = data[1]
+            if args.data == "cifar10":
+                label = paddle.reshape(label, [-1, 1])
 
             eval_start = time.time()
 
@@ -262,6 +269,8 @@ def compress(args):
 
             image = data[0]
             label = data[1]
+            if args.data == "cifar10":
+                label = paddle.reshape(label, [-1, 1])
 
             train_start = time.time()
             out = net(image)