Some experiments with TF, 3

.travis.yml

@@ -19,7 +19,7 @@ before_script:
     # stop the build if there are Python syntax errors or undefined names
     - flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
     # exit-zero treats all errors as warnings.  The GitHub editor is 127 chars wide
-    - flake8 . --count --exit-zero --max-complexity=32 --max-line-length=127 --ignore=F403,F405,E126,E127 --exclude=./pytorch/pytorchcv/models/others,./tensorflow_ --statistics
+    - flake8 . --count --exit-zero --max-complexity=32 --max-line-length=127 --ignore=F403,F405,E126,E127 --exclude=./pytorch/pytorchcv/models/others,./tensorflow_/others --statistics
 script:
     - true  # pytest --capture=sys  # add others tests here
 notifications:

eval_tf.py

@@ -1,12 +1,14 @@
 import argparse
+import tqdm
 import time
 import logging
 
-import mxnet as mx
+from tensorpack.predict import PredictConfig, FeedfreePredictor
+from tensorpack.utils.stats import RatioCounter
+from tensorpack.input_source import QueueInput, StagingInput
 
 from common.logger_utils import initialize_logging
-from tensorflow_.utils import prepare_tf_context, prepare_model, get_data_loader, calc_net_weight_count,\
-    validate
+from tensorflow_.utils import prepare_tf_context, prepare_model, get_data
 
 
 def parse_args():
@@ -78,29 +80,30 @@ def parse_args():
 
 
 def test(net,
-         val_data,
-         batch_fn,
-         use_rec,
-         dtype,
-         ctx,
-         calc_weight_count=False,
+         session_init,
+         val_dataflow,
          extended_log=False):
-    acc_top1 = mx.metric.Accuracy()
-    acc_top5 = mx.metric.TopKAccuracy(5)
+
+    pred_config = PredictConfig(
+        model=net,
+        session_init=session_init,
+        input_names=['input', 'label'],
+        output_names=['wrong-top1', 'wrong-top5']
+    )
+    err_top1 = RatioCounter()
+    err_top5 = RatioCounter()
 
     tic = time.time()
-    err_top1_val, err_top5_val = validate(
-        acc_top1=acc_top1,
-        acc_top5=acc_top5,
-        net=net,
-        val_data=val_data,
-        batch_fn=batch_fn,
-        use_rec=use_rec,
-        dtype=dtype,
-        ctx=ctx)
-    if calc_weight_count:
-        weight_count = calc_net_weight_count(net)
-        logging.info('Model: {} trainable parameters'.format(weight_count))
+    pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(val_dataflow), device='/gpu:0'))
+    for _ in tqdm.trange(val_dataflow.size()):
+        err_top1_val, err_top5_val = pred()
+        batch_size = err_top1_val.shape[0]
+        err_top1.feed(err_top1_val.sum(), batch_size)
+        err_top5.feed(err_top5_val.sum(), batch_size)
+
+    err_top1_val = err_top1.ratio
+    err_top5_val = err_top5.ratio
+
     if extended_log:
         logging.info('Test: err-top1={top1:.4f} ({top1})\terr-top5={top5:.4f} ({top5})'.format(
             top1=err_top1_val, top5=err_top5_val))
@@ -125,25 +128,20 @@ def main():
         num_gpus=args.num_gpus,
         batch_size=args.batch_size)
 
-    net = prepare_model(
+    net, inputs_desc = prepare_model(
         model_name=args.model,
         pretrained_model_file_path=args.resume.strip())
 
-    train_data, val_data, batch_fn = get_data_loader(
-        data_dir=args.data_dir,
+    val_dataflow = get_data(
+        is_train=False,
         batch_size=batch_size,
-        num_workers=args.num_workers)
+        data_dir_path=args.data_dir)
 
     assert (args.use_pretrained or args.resume.strip())
     test(
         net=net,
-        val_data=val_data,
-        batch_fn=batch_fn,
-        use_rec=args.use_rec,
-        dtype=args.dtype,
-        # ctx=ctx,
-        # calc_weight_count=(not log_file_exist),
-        calc_weight_count=True,
+        session_init=inputs_desc,
+        val_dataflow=val_dataflow,
         extended_log=True)