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Modernize and use more native TF APIs #150

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Modernize and use more native TF APIs #150

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12 changes: 7 additions & 5 deletions 4-Object_Detection/YOLOV3/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,10 +60,12 @@ def train_step(image_data, target):

gradients = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
tf.print("=> STEP %4d lr: %.6f giou_loss: %4.2f conf_loss: %4.2f "
"prob_loss: %4.2f total_loss: %4.2f" %(global_steps, optimizer.lr.numpy(),
giou_loss, conf_loss,
prob_loss, total_loss))
tf.print("=> STEP " + tf.strings.as_string(global_steps) +
"\tlr: " + tf.strings.as_string(optimizer.lr, 6) +
"\tgiou_loss: " + tf.strings.as_string(giou_loss, 2) +
"\tconf_loss: " + tf.strings.as_string(conf_loss, 2) +
"\tprob_loss: " + tf.strings.as_string(prob_loss, 2) +
"\ttotal_loss: " + tf.strings.as_string(total_loss, 2))
# update learning rate
global_steps.assign_add(1)
if global_steps < warmup_steps:
Expand All @@ -72,7 +74,7 @@ def train_step(image_data, target):
lr = cfg.TRAIN.LR_END + 0.5 * (cfg.TRAIN.LR_INIT - cfg.TRAIN.LR_END) * (
(1 + tf.cos((global_steps - warmup_steps) / (total_steps - warmup_steps) * np.pi))
)
optimizer.lr.assign(lr.numpy())
optimizer.lr.assign(tf.cast(lr, tf.float32))

# writing summary data
with writer.as_default():
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