add post_quant CE (#961)

demo/dygraph/post_quant/ptq.py

@@ -20,6 +20,7 @@ import contextlib
 import os
 import time
 import math
+import random
 import numpy as np
 from PIL import Image
 
@@ -28,7 +29,6 @@ from paddle.io import Dataset
 from paddle.vision.transforms import transforms
 import paddle.vision.models as models
 import paddle.nn as nn
-
 from paddleslim import PTQ
 
 import sys
@@ -41,7 +41,6 @@ from models.dygraph.mobilenet_v3 import MobileNetV3_large_x1_0
 class ImageNetValDataset(Dataset):
     def __init__(self, data_dir, image_size=224, resize_short_size=256):
         super(ImageNetValDataset, self).__init__()
-        train_file_list = os.path.join(data_dir, 'train_list.txt')
         val_file_list = os.path.join(data_dir, 'val_list.txt')
         test_file_list = os.path.join(data_dir, 'test_list.txt')
         self.data_dir = data_dir
@@ -68,9 +67,9 @@ class ImageNetValDataset(Dataset):
         return len(self.data)
 
 
-def calibrate(model, dataset, batch_num, batch_size):
+def calibrate(model, dataset, batch_num, batch_size, num_workers=5):
     data_loader = paddle.io.DataLoader(
-        dataset, batch_size=batch_size, num_workers=5)
+        dataset, batch_size=batch_size, num_workers=num_workers)
 
     for idx, data in enumerate(data_loader()):
         img = data[0]
@@ -85,6 +84,15 @@ def calibrate(model, dataset, batch_num, batch_size):
 
 
 def main():
+    num_workers = 5
+    if FLAGS.ce_test:
+        # set seed
+        seed = 111
+        paddle.seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
+        num_workers = 0
+
     # 1 load model
     model_list = [x for x in models.__dict__["__all__"]]
     model_list.append('mobilenet_v3')
@@ -126,8 +134,12 @@ def main():
     quant_model = ptq.quantize(fp32_model, fuse=FLAGS.fuse, fuse_list=fuse_list)
 
     print("Start calibrate...")
-    calibrate(quant_model, val_dataset, FLAGS.quant_batch_num,
-              FLAGS.quant_batch_size)
+    calibrate(
+        quant_model,
+        val_dataset,
+        FLAGS.quant_batch_num,
+        FLAGS.quant_batch_size,
+        num_workers=num_workers)
 
     # 3 save
     quant_output_dir = os.path.join(FLAGS.output_dir, FLAGS.model, "int8_infer",
@@ -172,6 +184,8 @@ if __name__ == '__main__':
         "--quant_batch_num", default=10, type=int, help="batch num for quant")
     parser.add_argument(
         "--quant_batch_size", default=10, type=int, help="batch size for quant")
+    parser.add_argument(
+        '--ce_test', default=False, type=bool, help="Whether to CE test.")
 
     FLAGS = parser.parse_args()
     assert FLAGS.data, "error: must provide data path"

demo/quant/quant_embedding/train.py

@@ -10,6 +10,7 @@ import paddle
 import six
 import reader
 from net import skip_gram_word2vec
+import paddle
 
 logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("paddle")
@@ -77,6 +78,12 @@ def parse_args():
         required=False,
         default=False,
         help='print speed or not , (default: False)')
+    parser.add_argument(
+        '--ce_test',
+        required=False,
+        default=False,
+        help='Whether to CE test, (default: False)')
+
     return parser.parse_args()
 
 
@@ -185,6 +192,12 @@ def GetFileList(data_path):
 
 
 def train(args):
+    if args.ce_test:
+        # set seed
+        seed = 111
+        paddle.seed(seed)
+        np.random.seed(seed)
+        random.seed(seed)
 
     if not os.path.isdir(args.model_output_dir):
         os.mkdir(args.model_output_dir)

demo/quant/quant_post/quant_post.py

@@ -6,7 +6,9 @@ import argparse
 import functools
 import math
 import time
+import random
 import numpy as np
+import paddle
 
 sys.path[0] = os.path.join(
     os.path.dirname("__file__"), os.path.pardir, os.path.pardir)
@@ -29,12 +31,13 @@ add_arg('params_filename',      str, None,                 "params file name")
 add_arg('algo',         str, 'hist',               "calibration algorithm")
 add_arg('hist_percent',         float, 0.9999,             "The percentile of algo:hist")
 add_arg('bias_correction',         bool, False,             "Whether to use bias correction")
+add_arg('ce_test',                 bool,   False,                                        "Whether to CE test.")
 
 # yapf: enable
 
 
 def quantize(args):
-    val_reader = reader.train()
+    val_reader = reader.val()
 
     place = paddle.CUDAPlace(0) if args.use_gpu else paddle.CPUPlace()
 
@@ -59,6 +62,12 @@ def quantize(args):
 def main():
     args = parser.parse_args()
     print_arguments(args)
+    if args.ce_test:
+        # set seed
+        seed = 111
+        np.random.seed(seed)
+        paddle.seed(seed)
+        random.seed(seed)
     quantize(args)