def get_train_and_test():
import utils.datasets as ds
datasets = ds.create_dataset()
dev = ds.get_X_dataset(datasets,
"../dataset/traindev/rumoureval-subtaskA-dev.json")
train = ds.get_X_dataset(
datasets, "../dataset/traindev/rumoureval-subtaskA-train.json")
return train, dev
def predict_tests():
# Test FashionAttrsDataset
csv_file = '/home/wangx/datasets/fashionAI/rank/Tests/question.csv'
root_dir = '/home/wangx/datasets/fashionAI/rank'
fashion_dataset = FashionAttrsDataset(csv_file, root_dir, mode='?')
assert_true(len(fashion_dataset) > 0)
# Test create_dataset
csv_file = '/home/wangx/project/torchfashion/questions/{}_{}.csv'
root_dir = '/home/wangx/datasets/fashionAI/rank'
for t in order:
out = create_dataset(label_type=t,
csv_file=csv_file,
root_dir=root_dir,
phase=['test'],
label_mode='?')
# Test image_datset and dataset_size
assert_true(
len(out['image_datasets']['test']) == out['dataset_sizes']['test'])
# Test dataloader
loader = out['dataloaders']['test']
batch = next(iter(loader))
assert_equal(list(batch['image'].shape), [32, 3, 224, 224])
# Test predict_model
out = create_dataset('coat_length_labels',
csv_file=csv_file,
root_dir=root_dir,
phase=['test'],
label_mode='?')
dataloader = out['dataloaders']['test']
model_conv = torchvision.models.resnet34()
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, AttrKey['coat_length_labels'])
use_gpu = torch.cuda.is_available()
if use_gpu:
model_conv = model_conv.cuda()
saved_model = './log/resnet34-transfer/coat_length_labels.pth'
result = predict_model(model_conv, saved_model, dataloader, use_gpu)
assert_equal(len(result), 1453)
def main():
str0 = "Dataset created in:"
chrono = Chronometer()
datasets = ds.create_dataset()
test = ds.get_X_dataset(
datasets, "../dataset/traindev/rumoureval-subtaskA-dev.json")
train = ds.get_X_dataset(
datasets, "../dataset/traindev/rumoureval-subtaskA-train.json")
train, all_words, freq = vc.vectorise_freq_train(train)
test = vc.vectorise_freq_test(test, all_words, freq)
train_vecs, train_labels = vc.get_vec_label(train)
test_vecs, test_labels = vc.get_vec_label(test)
str0 += chrono.str_flow_out()
chrono.rinit()
nb_model = nb.create_nb_model(train_vecs, train_labels)
pred_label = nb.nb_predict(nb_model, test_vecs, test_labels)
str0 += "\nModel Naive bayes:\n"
str0 += ms.all_mesure(pred_label)
str0 += chrono.str_flow_out() + "\n"
chrono.rinit()
# list_label = sorted(list(set(train_labels)))
# train_labels = nn.convert_label_in_number(train_labels, list_label)
nn_model = nn.create_trained_nn(train_vecs, train_labels, epochs=50)
# test_labels = nn.convert_label_in_number(test_labels, list_label)
pred_label = nn.convert_number_to_label(
nn.predict_nn(nn_model, test_vecs, test_labels), list_label)
str0 += "\nModel Neural Network:\nEpochs: 50\n"
str0 += ms.all_mesure(pred_label)
str0 += chrono.str_flow_out()
loss, acc = nn_model.evaluate(test_vecs, test_labels)
steps = [[loss, acc, chrono.stop(), 50]]
str0 += "\n\nAccuracy Keras:{} \nLoss Keras: {} \n".format(acc, loss)
for i in range(100, 1050, 50):
str0 += "\nEpochs: {}\n".format(i)
nn_model = nn.retrain_model(nn_model, train_vecs, train_labels, 50)
pred_label = nn.convert_number_to_label(
nn.predict_nn(nn_model, test_vecs, test_labels), list_label)
str0 += ms.all_mesure(pred_label)
str0 += chrono.str_flow_out()
loss, acc = nn_model.evaluate(test_vecs, test_labels)
steps.append([loss, acc, chrono.stop(), i])
str0 += "\n Accuracy Keras:{} \n Loss Keras: {} \n".format(acc, loss)
with open('result_server.txt', 'w') as stream:
json.dump(str0, stream)
with open('steps.json', 'w') as stream:
json.dump(steps, stream)
print(str0)
print(chrono.str_flow_out())
def datasets_tests():
csv_file = '/home/wangx/datasets/fashionAI/web/Annotations/skirt_length_labels_train.csv'
root_dir = '/home/wangx/datasets/fashionAI/web/'
# Test FashionAttrsDataset
fashion_dataset = FashionAttrsDataset(csv_file, root_dir)
assert_true(len(fashion_dataset)>0)
# Test create_dataset
datasetd = create_dataset('skirt_length_labels')
# Test image_datasets
image_datasets = datasetd['image_datasets']
for _, image_dataset in image_datasets.items():
assert_true(len(image_dataset) > 0)
# Test dataloaders
dataloaders = datasetd['dataloaders']
for _, dataloader in dataloaders.items():
next_batch = next(iter(dataloader))
assert_equal(list(next_batch['image'].shape), [32, 3, 224, 224])
'skirt_length_labels': 'resnet18_c',
'sleeve_length_labels': 'inceptionresnetv2_c',
}
saved_model = './log/{}/{}.pth'
question_file = './questions_b/{}_{}.csv'
root_dir = '/home/wangx/datasets/fashionAI/z_rank'
answer = './questions_b/' + args.answer + '.csv'
# Iterate each attributes
for t in order:
# Create dataloader for each attribute
out = create_dataset(t,
csv_file=question_file,
root_dir=root_dir,
phase=['test'],
label_mode='?',
shuffle=False,
img_size=ImgSizeKey[t],
batch_size=8)
dataloader = out['dataloaders']['test']
# Create CNN model
use_gpu = torch.cuda.is_available()
model_conv = create_model(model_key=ModelKey[t],
pretrained=False,
num_of_classes=AttrKey[t],
use_gpu=use_gpu)
print('start write {}...'.format(t))
result = predict_model(model_conv, saved_model.format(SaveFolderKey[t], t),
dataloader, use_gpu)
'coat_length_labels': 8,
'collar_design_labels': 5,
'lapel_design_labels': 5,
'neck_design_labels': 5,
'neckline_design_labels': 10,
'pant_length_labels': 6,
'skirt_length_labels': 6,
'sleeve_length_labels': 9,
}
# Create dataloader
root_dir = '/home/wangx/datasets/fashionAI/base/'
csv_file = './data/' + args.csv_folder + '/{}_{}.csv'
out = create_dataset(args.attribute,
csv_file=csv_file,
root_dir=root_dir,
img_size=args.img_size,
batch_size=args.batch_size)
dataloaders = out['dataloaders']
dataset_sizes = out['dataset_sizes']
# Create CNN model
use_gpu = torch.cuda.is_available()
model_conv = create_model(
model_key=args.model,
pretrained=eval(args.pretrained),
num_of_classes=AttrKey[args.attribute],
use_gpu=use_gpu,
)
criterion = nn.CrossEntropyLoss()