Fix TF exports >= 2GB (#6292)

export.py

@@ -247,11 +247,11 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
 
 def export_saved_model(model, im, file, dynamic,
                        tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
-                       conf_thres=0.25, prefix=colorstr('TensorFlow SavedModel:')):
+                       conf_thres=0.25, keras=False, prefix=colorstr('TensorFlow SavedModel:')):
     # YOLOv5 TensorFlow SavedModel export
     try:
         import tensorflow as tf
-        from tensorflow import keras
+        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
 
         from models.tf import TFDetect, TFModel
 
@@ -262,13 +262,26 @@ def export_saved_model(model, im, file, dynamic,
         tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
         im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
         _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-        inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
+        inputs = tf.keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
         outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-        keras_model = keras.Model(inputs=inputs, outputs=outputs)
+        keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
         keras_model.trainable = False
         keras_model.summary()
-        keras_model.save(f, save_format='tf')
-
+        if keras:
+            keras_model.save(f, save_format='tf')
+        else:
+            m = tf.function(lambda x: keras_model(x))  # full model
+            spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
+            m = m.get_concrete_function(spec)
+            frozen_func = convert_variables_to_constants_v2(m)
+            tfm = tf.Module()
+            tfm.__call__ = tf.function(lambda x: frozen_func(x), [spec])
+            tfm.__call__(im)
+            tf.saved_model.save(
+                tfm,
+                f,
+                options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if
+                check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
         LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
         return keras_model, f
     except Exception as e:

models/common.py

@@ -359,7 +359,8 @@ class DetectMultiBackend(nn.Module):
             if saved_model:  # SavedModel
                 LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
                 import tensorflow as tf
-                model = tf.keras.models.load_model(w)
+                keras = False  # assume TF1 saved_model
+                model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
             elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
                 LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
                 import tensorflow as tf
@@ -431,7 +432,7 @@ class DetectMultiBackend(nn.Module):
         else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
             im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
             if self.saved_model:  # SavedModel
-                y = self.model(im, training=False).numpy()
+                y = (self.model(im, training=False) if self.keras else self.model(im)[0]).numpy()
             elif self.pb:  # GraphDef
                 y = self.frozen_func(x=self.tf.constant(im)).numpy()
             elif self.tflite:  # Lite