Remove test_mnist, since we replace it with compile time concepts (#5144)

python/paddle/v2/framework/tests/test_mnist.py

-import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
-import numpy
-import paddle.v2 as paddle
-exit(
-    0
-)  # FIXME(yuyang18): InferShape has been removed, this unittest should be changed until compile time is ready
-
-BATCH_SIZE = 100
-
-scope = core.Scope()
-place = core.CPUPlace()
-# if you want to test GPU training, you can use gpu place
-# place = core.GPUPlace(0)
-dev_ctx = core.DeviceContext.create(place)
-
-init_net = core.Net.create()
-forward_net = core.Net.create()
-backward_net = None
-optimize_net = core.Net.create()
-
-
-def atomic_id():
-    id = 0
-    while True:
-        yield id
-        id += 1
-
-
-uniq_id = atomic_id().next
-
-
-def data_layer(name, dims):
-    var = scope.var(name)
-    tensor = var.get_tensor()
-    tensor.set_dims(dims)  # 1 is batch size holder.
-    return name
-
-
-def feed_data(name, data):
-    assert isinstance(data, numpy.ndarray)
-    tensor = scope.find_var(name).get_tensor()
-    tensor.set_dims(data.shape)
-    if data.dtype == numpy.dtype("int32"):
-        tensor.alloc_int(place)
-    elif data.dtype == numpy.dtype("float32"):
-        tensor.alloc_float(place)
-    else:
-        raise ValueError("data type not supported")
-    tensor.set(data, place)
-
-
-def grad_var_name(var_name):
-    return var_name + "@GRAD"
-
-
-def sgd_optimizer(net, param_name, learning_rate=0.005):
-    grad_name = grad_var_name(param_name)
-    optimize_op = Operator(
-        "sgd",
-        param=param_name,
-        grad=grad_name,
-        param_out=param_name,
-        learning_rate=learning_rate)
-    net.append_op(optimize_op)
-
-
-# should use operator and add these to the init_network
-def init_param(net, param_name, dims):
-    scope.var(param_name)
-    op = Operator(
-        "uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10)
-    op.infer_shape(scope)
-    net.append_op(op)
-
-
-# fc_layer
-def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
-    """
-    The fully connected layer.
-
-    :param input: The name of input variable.
-    :type input: str
-    :param size: The size of fully connected layer.
-    :param act: The name of activation.
-    :param param: The attribute of learnable parameter which can be used to
-                  modify initialization mean and std of the parameter.
-    :param bias: The attribute of bias. If set False, this layer does not have
-                 a bias.
-    :param name: The name of this layer. If it is not set explictly, a name
-                 will be generated automatically.
-    :return: The name of the output variable.
-    """
-
-    if name is None:
-        name = "fc_%d" % uniq_id()
-    if not isinstance(name, str):
-        raise ValueError("The name of a layer should be a string.")
-
-    input_dims = scope.find_var(input).get_tensor().get_dims()
-
-    w_name = param or name + ".w"
-    init_param(net=init_net, param_name=w_name, dims=[input_dims[1], size])
-    sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01)
-
-    pre_activation = name + ".mul.out"
-    scope.var(pre_activation)
-    mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation)
-    net.append_op(mul_op)
-
-    # create bias variable if needed
-    if bias:
-        bias_name = name + ".b"
-        init_param(net=init_net, param_name=bias_name, dims=[size])
-        sgd_optimizer(
-            net=optimize_net, param_name=bias_name, learning_rate=0.001)
-        bias_out = name + ".rowwise_add.out"
-        scope.var(bias_out)
-        rowwise_append_op = Operator(
-            "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out)
-        net.append_op(rowwise_append_op)
-        pre_activation = bias_out
-
-    activation_op = Operator(act, X=pre_activation, Y=name)
-    net.append_op(activation_op)
-    scope.var(name)
-    net.infer_shape(scope)
-    return name
-
-
-def cross_entropy_layer(net, input, label):
-    cost_name = "cross_entropy_%d" % uniq_id()
-    cross_entropy_op = Operator(
-        "cross_entropy", X=input, Label=label, Y=cost_name)
-    net.append_op(cross_entropy_op)
-    scope.var(cost_name)
-    net.infer_shape(scope)
-    return cost_name
-
-
-def create_backward_net(forward_net):
-    net = core.Operator.backward(forward_net, set())
-    for input in net.inputs()["all"]:
-        var = scope.var(input)
-        var.get_tensor()
-    for output in net.outputs()["all"]:
-        var = scope.var(output)
-        var.get_tensor()
-    return net
-
-
-def debug_print_op(op):
-    print("===============" + op.type() + "==============")
-    print("***inputs:***")
-    for input in op.inputs()["all"]:
-        print input, scope.find_var(input).get_tensor().get_dims()
-    print("\n***outputs:***")
-    for output in op.outputs()["all"]:
-        print output, scope.find_var(output).get_tensor().get_dims()
-    print("")
-    print("")
-
-
-def set_cost(cost):
-    cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape
-    cost_grad = \
-        scope.find_var(grad_var_name(cost)).get_tensor()
-    cost_grad.set_dims(cost_shape)
-    cost_grad.alloc_float(place)
-    cost_grad.set(numpy.ones(cost_shape).astype("float32"), place)
-
-
-def get_cost_mean(cost):
-    cost_data = numpy.array(scope.find_var(cost).get_tensor())
-    return cost_data.sum() / len(cost_data)
-
-
-def error_rate(predict, label):
-    predict_var = numpy.array(scope.find_var(predict).get_tensor()).argmax(
-        axis=1)
-    label = numpy.array(scope.find_var(label).get_tensor())
-    error_num = numpy.sum(predict_var != label)
-    return error_num / float(len(label))
-
-
-images = data_layer(name="pixel", dims=[BATCH_SIZE, 784])
-labels = data_layer(name="label", dims=[BATCH_SIZE, 1])
-fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid")
-fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid")
-predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax")
-cost = cross_entropy_layer(net=forward_net, input=predict, label=labels)
-
-init_net.complete_add_op(True)
-forward_net.complete_add_op(True)
-backward_net = create_backward_net(forward_net)
-optimize_net.complete_add_op(True)
-
-print(init_net)
-print(forward_net)
-print(backward_net)
-print(optimize_net)
-
-debug_print_op(forward_net)
-debug_print_op(backward_net)
-debug_print_op(optimize_net)
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.mnist.train(), buf_size=8192),
-    batch_size=BATCH_SIZE)
-
-
-def test(cost_name):
-    test_reader = paddle.batch(
-        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
-    cost = []
-    error = []
-    for data in test_reader():
-        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
-        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
-        label_data = numpy.expand_dims(label_data, axis=1)
-        feed_data(images, image_data)
-        feed_data(labels, label_data)
-
-        forward_net.infer_shape(scope)
-        forward_net.run(scope, dev_ctx)
-        cost.append(get_cost_mean(cost_name))
-        error.append(error_rate(predict, "label"))
-    print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str(
-        sum(error) / float(len(error))))
-
-
-PASS_NUM = 1
-
-init_net.run(scope, dev_ctx)
-for pass_id in range(PASS_NUM):
-    batch_id = 0
-
-    for data in train_reader():
-        image_data = numpy.array(map(lambda x: x[0], data)).astype("float32")
-        label_data = numpy.array(map(lambda x: x[1], data)).astype("int32")
-        label_data = numpy.expand_dims(label_data, axis=1)
-        feed_data(images, image_data)
-        feed_data(labels, label_data)
-
-        forward_net.infer_shape(scope)
-        forward_net.run(scope, dev_ctx)
-        set_cost(cost)
-        backward_net.infer_shape(scope)
-        backward_net.run(scope, dev_ctx)
-
-        optimize_net.run(scope, dev_ctx)
-        if batch_id % 100 == 0:
-            print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]")
-            test(cost)
-
-        batch_id = batch_id + 1