N_ANCHORS = Configuration.ANCHORS.shape[0]
GRID_SIZE = Configuration.GRID_SIZE
#%%
#Load Model along with weights
model = get_pre_trained_model()
model.load_weights(
'trained_weights\leaf_data_v2\model_weights_16-nosoftmax.h5')
model.summary()
#%%
dataset = ImageDataset('data/leaf_data_v2/train',
'data/leaf_data_v2/test',
shuffle=True,
histogram_equ=False,
brightness_range=None)
#%%
x, y = dataset.next_test_batch()
#%%
y_pred = model.predict(x)
#%%
y_pred_1 = y_pred[0]
x_1 = x[0]
#%%
y_pred_1 = np.reshape(y_pred_1,
(GRID_SIZE * GRID_SIZE * N_ANCHORS, N_CLASSES + 5))
for i in range(y_pred_1.shape[0]):
y_pred_1[i, 0:4] = util.cvt_coord_to_diagonal(y_pred_1[i, 0:4])
y_pred_1[:, 0:4] = np.clip(y_pred_1[:, 0:4], a_min=0.0, a_max=1.0)
#%%
y_pred_1 = non_max_suppression(y_pred_1, 0.5, 0.3)
#%%
img = x_1
'data/leaf_data_v2/test',
shuffle=True)
x, y = dataset.next_train_batch()
#%%
for i in range(EPOCHS):
print("Epoch ", i + 1)
for j in range(STEPS_PER_EPOCH):
X, y = dataset.next_train_batch()
X = np.reshape(X,
newshape=(BATCH_SIZE, Configuration.IMAGE_HEIGHT,
Configuration.IMAGE_WIDTH, 3))
y = np.reshape(y,
newshape=(BATCH_SIZE, GRID_SIZE, GRID_SIZE,
PREDICTIONS_PER_CELL))
model.train_on_batch(X, y)
if (j) % 100 == 0:
print("Step {}".format(j))
X_test, y_test = dataset.next_test_batch()
X_test = np.reshape(X_test,
(BATCH_SIZE, Configuration.IMAGE_HEIGHT,
Configuration.IMAGE_WIDTH, 3))
y_test = np.reshape(
y_test,
(BATCH_SIZE, GRID_SIZE, GRID_SIZE, PREDICTIONS_PER_CELL))
l1 = model.evaluate(X_test, y_test)
print('Val Loss:{}'.format(l1))
model.save_weights('trained_weights/leaf_v2_model.h5')
#%%
print("Finished Training")
model.save_weights('trained_weigths/leaf_v2_model.h5')
