"""
Copyright 2020 The OneFlow Authors. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from oneflow.compatible import single_client as flow

BLOCK_COUNTS = [3, 4, 6, 3]
BLOCK_FILTERS = [256, 512, 1024, 2048]
BLOCK_FILTERS_INNER = [64, 128, 256, 512]


def _conv2d(
    name,
    input,
    filters,
    kernel_size,
    strides=1,
    padding="SAME",
    data_format="NCHW",
    dilations=1,
    trainable=True,
    weight_initializer=flow.variance_scaling_initializer(data_format="NCHW"),
):
    weight = flow.get_variable(
        name + "-weight",
        shape=(filters, input.shape[1], kernel_size, kernel_size),
        dtype=input.dtype,
        initializer=weight_initializer,
        trainable=trainable,
    )
    return flow.nn.conv2d(
        input, weight, strides, padding, None, data_format, dilations, name=name
    )


def _batch_norm(inputs, name=None, trainable=True):
    return flow.layers.batch_normalization(
        inputs=inputs,
        axis=1,
        momentum=0.997,
        epsilon=1.001e-05,
        center=True,
        scale=True,
        trainable=trainable,
        name=name,
    )


def conv2d_affine(input, name, filters, kernel_size, strides, activation=None):
    padding = "SAME" if strides > 1 or kernel_size > 1 else "VALID"
    output = _conv2d(name, input, filters, kernel_size, strides, padding)
    output = _batch_norm(output, name + "_bn")
    if activation == "Relu":
        output = flow.math.relu(output)
    return output


def bottleneck_transformation(input, block_name, filters, filters_inner, strides):
    a = conv2d_affine(
        input, block_name + "_branch2a", filters_inner, 1, 1, activation="Relu"
    )
    b = conv2d_affine(
        a, block_name + "_branch2b", filters_inner, 3, strides, activation="Relu"
    )
    c = conv2d_affine(b, block_name + "_branch2c", filters, 1, 1)
    return c


def residual_block(input, block_name, filters, filters_inner, strides_init):
    if strides_init != 1 or block_name == "res2_0":
        shortcut = conv2d_affine(
            input, block_name + "_branch1", filters, 1, strides_init
        )
    else:
        shortcut = input
    bottleneck = bottleneck_transformation(
        input, block_name, filters, filters_inner, strides_init
    )
    return flow.math.relu(bottleneck + shortcut)


def residual_stage(input, stage_name, counts, filters, filters_inner, stride_init=2):
    output = input
    for i in range(counts):
        block_name = "%s_%d" % (stage_name, i)
        output = residual_block(
            output, block_name, filters, filters_inner, stride_init if i == 0 else 1
        )
    return output


def resnet_conv_x_body(input, on_stage_end=lambda x: x):
    output = input
    for (i, (counts, filters, filters_inner)) in enumerate(
        zip(BLOCK_COUNTS, BLOCK_FILTERS, BLOCK_FILTERS_INNER)
    ):
        stage_name = "res%d" % (i + 2)
        output = residual_stage(
            output, stage_name, counts, filters, filters_inner, 1 if i == 0 else 2
        )
        on_stage_end(output)
    return output


def resnet_stem(input):
    conv1 = _conv2d("conv1", input, 64, 7, 2)
    conv1_bn = flow.math.relu(_batch_norm(conv1, "conv1_bn"))
    pool1 = flow.nn.max_pool2d(
        conv1_bn, ksize=3, strides=2, padding="VALID", data_format="NCHW", name="pool1"
    )
    return pool1


def resnet50(images, trainable=True):
    with flow.scope.namespace("Resnet"):
        stem = resnet_stem(images)
        body = resnet_conv_x_body(stem, lambda x: x)
        pool5 = flow.nn.avg_pool2d(
            body, ksize=7, strides=1, padding="VALID", data_format="NCHW", name="pool5"
        )
        fc1001 = flow.layers.dense(
            flow.reshape(pool5, (pool5.shape[0], -1)),
            units=1001,
            use_bias=True,
            kernel_initializer=flow.xavier_uniform_initializer(),
            bias_initializer=flow.zeros_initializer(),
            trainable=trainable,
            name="fc1001",
        )
    return fc1001
