def main():
dir_path = sys.argv[1]
model_path = sys.argv[2]
phi = 0
weighted_bifpn = True
freeze_backbone = False
#Load test data
images, heatmaps = get_testData(dir_path)
tf.compat.v1.keras.backend.set_session(get_session())
pred_model = efficientdet(phi,
weighted_bifpn=weighted_bifpn,
freeze_bn=freeze_backbone)
print("Load model ........ \n")
pred_model.load_weights(model_path, by_name=True)
pred_model.compile(optimizer=Adam(lr=1e-3), loss='mse')
preds = pred_model.predict(images)
save_preds(dir_path, preds)
def main():
dir_path = sys.argv[1]
phi = 0
cont_training = False
weighted_bifpn = True
freeze_backbone = False
tf.compat.v1.keras.backend.set_session(get_session())
# create the generators
# train_generator = trainGenerator(dir_path)
images, heatmaps = get_trainData(dir_path, multi_dim=True)
print("Number of images: %s and heatmaps: %s\n" %
(len(images), len(heatmaps)))
model = efficientdet(phi,
weighted_bifpn=weighted_bifpn,
freeze_bn=freeze_backbone)
# model_name = 'efficientnet-b{}'.format(phi)
# file_name = '{}_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5'.format(model_name)
# file_hash = WEIGHTS_HASHES[model_name][1]
# weights_path = keras.utils.get_file(file_name,
# BASE_WEIGHTS_PATH + file_name,
# cache_subdir='models',
# file_hash=file_hash)
# model.load_weights(weights_path, by_name=True)
# freeze backbone layers
if freeze_backbone:
# 227, 329, 329, 374, 464, 566, 656
for i in range(1, [227, 329, 329, 374, 464, 566, 656][phi]):
model.layers[i].trainable = False
# compile model
print("Compiling model ... \n")
# # SOFTMAX ACTIVATION
# model.compile(optimizer=Adam(lr=1e-3),
# loss=[categorical_focal_loss(gamma = 2, alpha = 0.25)])
# SIGMOID ACTIVATION
focalloss = SigmoidFocalCrossEntropy(
reduction=Reduction.SUM_OVER_BATCH_SIZE)
model.compile(optimizer=Adam(lr=1e-3), loss=focalloss)
# # LINEAR ACTIVATION
# model.compile(optimizer=Adam(lr=1e-3),
# loss='mean_absolute_error')
# print(model.summary())
# start training
# return model.fit_generator(
# generator=train_generator,
# steps_per_epoch=10,
# initial_epoch=0,
# epochs=10,
# verbose=1
# validation_data=validation_generator
# )
## 'efficientdet' for the first stacked heatmaps
if cont_training:
model.load_weights('efficientdet2')
model.fit(images, heatmaps, batch_size=16, epochs=60, verbose=1)
else:
model.fit(images, heatmaps, batch_size=16, epochs=10, verbose=1)
model.save_weights('efficientdet2')
preds = model.predict(images[0:3])
# save_preds(dir_path, preds)
# fig = plt.figure()
plt.subplot(1, 2, 1)
plt.imshow(np.sum(preds[0], axis=-1))
plt.subplot(1, 2, 2)
plt.imshow(np.sum(heatmaps[0], axis=-1))
plt.show()
plt.savefig("testres.png")
def main():
dir_path = sys.argv[1]
phi = 0
cont_training = False
weighted_bifpn = True
freeze_backbone = False
input_shape = (320, 320, 3)
tf.compat.v1.keras.backend.set_session(get_session())
# images, heatmaps, heatmaps2,heatmaps3, coord = get_trainData(dir_path, 100, multi_dim=True)
traingen = dataGenerator_RHD(dir_path, batch_size=8, data_set='training')
validgen = dataGenerator_RHD(dir_path, batch_size=8, data_set='validation')
# check if it looks good
# plot_heatmaps_with_coords(images, heatmaps, coord)
# print("Number of images: %s and heatmaps: %s\n" % (len(images), len(heatmaps)))
model = efficientdet(phi,
input_shape=input_shape,
weighted_bifpn=weighted_bifpn,
freeze_bn=freeze_backbone)
# model = efficientdet_coord(phi, weighted_bifpn=weighted_bifpn,
# freeze_bn = freeze_backbone)
# freeze backbone layers
if freeze_backbone:
# 227, 329, 329, 374, 464, 566, 656
for i in range(1, [227, 329, 329, 374, 464, 566, 656][phi]):
model.layers[i].trainable = False
# compile model
print("Compiling model ... \n")
losses = {
"normalsize": weighted_bce,
"size2": weighted_bce,
'size3': weighted_bce
}
# losses = {"normalsize" : weighted_bce, "size2" : weighted_bce, 'size3':weighted_bce, 'depth' : 'mean_squared_error'}
# losses = {"normalsize" : weighted_bce, "size2" : weighted_bce, 'size3':weighted_bce, 'depthmaps' : 'mean_squared_error'}
lossWeights = {"normalsize": 1.0, "size2": 1.0, 'size3': 1.0}
# lossWeights = {"normalsize" : 1.0, "size2" : 1.0, 'size3' : 1.0, 'depth' : 1.0}
# lossWeights = {"normalsize" : 1.0, "size2" : 1.0, 'size3' : 1.0, 'depthmaps' : 1.0}
# focalloss = SigmoidFocalCrossEntropy(reduction=Reduction.SUM_OVER_BATCH_SIZE)
model.compile(optimizer=Adam(lr=1e-3),
loss=losses,
loss_weights=lossWeights,
metrics={'normalsize': 'mse'})
# model.compile(optimizer='adam', metrics=['accuracy'], loss=weighted_bce)
# loss=tf.keras.losses.SigmoidFocalCrossEntropy())
# loss=weighted_bce)
# loss=tf.keras.losses.SigmoidFocalCrossEntropy())
# loss=[focal_loss(gamma = 2, alpha = 0.25)])
# loss = 'mean_absolute_error'
# print(model.summary())
print("Number of parameters in the model : ", model.count_params())
# print(get_flops(model))
# model.fit(images, {"normalsize" : heatmaps, "size2": heatmaps2, 'size3': heatmaps3},
# batch_size=16, epochs=100, verbose=1)
# K.set_value(model.optimizer.learning_rate, 1e-5)
# model.fit(images, heatmaps, batch_size = 16, epochs = 100, verbose = 1)
# callbacks = [
# keras.callbacks.ModelCheckpoint(
# filepath='mymodel_{epoch}',
# # Path where to save the model
# # The two parameters below mean that we will overwrite
# # the current checkpoint if and only if
# # the `val_loss` score has improved.
# save_best_only=True,
# monitor='val_loss',
# verbose=1)
# ]
model.fit(traingen,
validation_data=validgen,
validation_steps=18,
steps_per_epoch=1,
epochs=1,
verbose=1)
# model.save_weights('handposenet')
# images = get_evalImages(dir_path, 10)
validgen2 = dataGenerator_RHD(dir_path,
batch_size=10,
data_set='validation')
(images, targets) = next(validgen2)
(preds, preds2, preds3) = model.predict(images)
# (preds, preds2 ,preds3, depth) = model.predict(images)
(heatmaps, heatmaps2, heatmaps3) = targets
# plot_acc_loss(history)
# get coordinates from predictions
coord_preds = heatmaps_to_coord(preds)
coord = heatmaps_to_coord(heatmaps)
# coord_upsamp = heatmaps_to_coord(preds2)
plot_predicted_heatmaps(preds, heatmaps)
# plot_predicted_heatmaps(preds2, heatmaps2)
plot_predicted_hands_uv(images, coord_preds * 4)
# xyz_pred = add_depth_to_coords(coord_preds[0], depth[0])
plot_predicted_coordinates(images, coord_preds * 4, coord * 4)