Unverified 提交 3beb871b authored 作者: Jiacong Fang's avatar Jiacong Fang 提交者: GitHub

Multiple TF export improvements (#4824)

* Add fused conv support * Set all saved_model values to non trainable * Fix TFLite fp16 model export * Fix int8 TFLite conversion
上级 6b44ecd5
...@@ -145,6 +145,7 @@ def export_saved_model(model, im, file, dynamic, ...@@ -145,6 +145,7 @@ def export_saved_model(model, im, file, dynamic,
inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) inputs = 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) 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 = keras.Model(inputs=inputs, outputs=outputs)
keras_model.trainable = False
keras_model.summary() keras_model.summary()
keras_model.save(f, save_format='tf') keras_model.save(f, save_format='tf')
...@@ -183,15 +184,17 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te ...@@ -183,15 +184,17 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
print(f'\n{prefix} starting export with tensorflow {tf.__version__}...') print(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
batch_size, ch, *imgsz = list(im.shape) # BCHW batch_size, ch, *imgsz = list(im.shape) # BCHW
f = file.with_suffix('.tflite') f = str(file).replace('.pt', '-fp16.tflite')
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
converter.target_spec.supported_types = [tf.float16]
converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.optimizations = [tf.lite.Optimize.DEFAULT]
if int8: if int8:
dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib) converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.target_spec.supported_types = []
converter.inference_input_type = tf.uint8 # or tf.int8 converter.inference_input_type = tf.uint8 # or tf.int8
converter.inference_output_type = tf.uint8 # or tf.int8 converter.inference_output_type = tf.uint8 # or tf.int8
converter.experimental_new_quantizer = False converter.experimental_new_quantizer = False
...@@ -249,7 +252,7 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' ...@@ -249,7 +252,7 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
# Load PyTorch model # Load PyTorch model
device = select_device(device) device = select_device(device)
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
model = attempt_load(weights, map_location=device, inplace=True, fuse=not any(tf_exports)) # load FP32 model model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
nc, names = model.nc, model.names # number of classes, class names nc, names = model.nc, model.names # number of classes, class names
# Input # Input
......
...@@ -70,8 +70,9 @@ class TFConv(keras.layers.Layer): ...@@ -70,8 +70,9 @@ class TFConv(keras.layers.Layer):
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
conv = keras.layers.Conv2D( conv = keras.layers.Conv2D(
c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False, c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy())) kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
......
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