Failed to convert SparseTensor to Tensor #42916
Description
I'm using tensorflow==2.3.0 and keras==2.4.3 on ubunto 20.04
the code work okay on tensorflow==2.1.0 and keras==2.3.1
class Args():
def __init__(self,dataset_path="../datasets/train" ,mymodel="outputs/my_model.h5", label="outputs/le.pickle", embeddings="outputs/embeddings.pickle", image_out="../datasets/test/img_test.jpg", image_in="../datasets/test/001.jpg", video_out="../datasets/videos_output/test.mp4", video_in="../datasets/videos_input/Ok_Arya_Stark.mp4", image_size='112,112', model='../models/arcface_r100_v1/model,0', ga_model='', detector='', gpu=0, det=0, flip=0, threshold=1.24):
self.dataset=dataset_path
self.mymodel=mymodel
self.le=label
self.embeddings=embeddings
self.image_out=image_out
self.image_in=image_in
self.video_out=video_out
self.video_in=video_in
self.image_size=image_size
self.model=model
self.ga_model=ga_model
self.detector=detector
self.gpu=gpu
self.det=det
self.flip=flip
self.threshold=threshold
def init_parsearges(self):
ap = argparse.ArgumentParser()
# Argument of insightface
ap.add_argument("--dataset", default=self.dataset,
help="Path to training dataset")
ap.add_argument("--mymodel", default=self.mymodel,
help="Path to recognizer model")
ap.add_argument("--le", default=self.le,
help="Path to label encoder")
ap.add_argument("--embeddings", default=self.embeddings,
help='Path to embeddings')
ap.add_argument("--image-out", default=self.image_out,
help='Path to output image')
ap.add_argument("--image-in", default=self.image_in,
help='Path to output image')
ap.add_argument("--video-out", default=self.video_out,
help='Path to output video')
ap.add_argument("--video-in", default=self.video_in)
ap.add_argument('--image-size', default=self.image_size, help='')
ap.add_argument('--model', default=self.model, help='path to load model.')
ap.add_argument('--ga-model', default=self.ga_model, help='path to load model.')
ap.add_argument('--detector', default=self.detector, type=str, help='face detector name')
ap.add_argument('--gpu', default=self.gpu, type=int, help='gpu id')
ap.add_argument('--det', default=self.det, type=int, help='mtcnn option, 1 means using R+O, 0 means detect from begining')
ap.add_argument('--flip', default=self.flip, type=int, help='whether do lr flip aug')
ap.add_argument('--threshold', default=self.threshold, type=float, help='ver dist threshold')
args = ap.parse_args()
return args
class SoftMax():
def __init__(self, input_shape, num_classes):
self.input_shape = input_shape
self.num_classes = num_classes
def build(self):
from keras.losses import categorical_crossentropy
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Dense, Dropout
# create model
model = Sequential()
# add model layers
model.add(Dense(1024, activation='relu', input_shape=self.input_shape))
model.add(Dropout(0.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.num_classes, activation='softmax'))
# loss and optimizer
optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss=categorical_crossentropy,
optimizer=optimizer,
metrics=['accuracy'])
return model
def make_model(args, classifier=SoftMax):
# Load the face embeddings
data = pickle.loads(open(args.embeddings, "rb").read())
num_classes = len(np.unique(data["names"]))
ct = ColumnTransformer([('myٔName', OneHotEncoder(), [0])])
labels = np.array(data["names"]).reshape(-1, 1)
labels = ct.fit_transform(labels)
embeddings = np.array(data["embeddings"])
# Initialize Softmax training model arguments
BATCH_SIZE = 32
EPOCHS = 32
input_shape = embeddings.shape[1]
# Build classifier
init_classifier = classifier(input_shape=(input_shape,), num_classes=num_classes)
model = init_classifier.build()
# Create KFold
cv = KFold(n_splits = 5, random_state = None, shuffle=True)
history = {'acc': [], 'val_acc': [], 'loss': [], 'val_loss': []}
# Train
for train_idx, valid_idx in cv.split(embeddings):
X_train, X_val, y_train, y_val = embeddings[train_idx], embeddings[valid_idx], labels[train_idx], labels[valid_idx]
his = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, validation_data=(X_val, y_val))
# write the face recognition model to output
model.save(args.mymodel)
f = open(args.le, "wb")
f.write(pickle.dumps(LabelEncoder()))
f.close()
I get the error:
---> 28 his = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=1, validation_data=(X_val, y_val))
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> to Tensor. Contents: SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(None, 2), dtype=int64), values=Tensor("DeserializeSparse:1", shape=(None,), dtype=float32), dense_shape=Tensor("stack:0", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
I have used several issues solutions but none have worked.