#############################################################
ctx = [mx.cpu(i) for i in range(cpu_count)]
##############################################################
train_ds = IAMDataset("form_bb",
output_data="bb",
output_parse_method=detection_box,
train=True)
print("Number of training samples: {}".format(len(train_ds)))
test_ds = IAMDataset("form_bb",
output_data="bb",
output_parse_method=detection_box,
train=False)
print("Number of testing samples: {}".format(len(test_ds)))
train_data = gluon.data.DataLoader(train_ds.transform(augment_transform),
batch_size,
shuffle=True,
last_batch="rollover",
num_workers=8)
test_data = gluon.data.DataLoader(test_ds.transform(transform),
batch_size,
shuffle=False,
last_batch="keep",
num_workers=8)
#%%
net = SSD(num_classes=2, ctx=ctx)
net.hybridize()
log_dir = "./logs/handwriting_recognition"
checkpoint_dir = "model_checkpoint"
checkpoint_name = "handwriting.params"
#%%
train_ds = IAMDataset("line", output_data="text", train=True)
print("Number of training samples: {}".format(len(train_ds)))
test_ds = IAMDataset("line", output_data="text", train=False)
print("Number of testing samples: {}".format(len(test_ds)))
#%%
train_data = gluon.data.DataLoader(train_ds.transform(augment_transform), batch_size, shuffle=True, last_batch="rollover", num_workers=4)
test_data = gluon.data.DataLoader(test_ds.transform(transform), batch_size, shuffle=True, last_batch="keep", num_workers=4)#, num_workers=multiprocessing.cpu_count()-2)
#%%
net = CNNBiLSTM(num_downsamples=num_downsamples, resnet_layer_id=resnet_layer_id , rnn_hidden_states=lstm_hidden_states, rnn_layers=lstm_layers, max_seq_len=max_seq_len, ctx=ctx)
net.hybridize()
ctc_loss = gluon.loss.CTCLoss(weight=0.2)
best_test_loss = 10e5
if (os.path.isfile(os.path.join(checkpoint_dir, checkpoint_name))):
net.load_parameters(os.path.join(checkpoint_dir, checkpoint_name))
print("Parameters loaded")
print(run_epoch(0, net, test_data, None, log_dir, print_name="pretrained", is_train=False))