Create ocnet.py

pdseg/models/modeling/ocnet.py

+# coding: utf8
+# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+
+from utils.config import cfg
+
+
+def conv_bn_layer(input, filter_size, num_filters, stride=1, padding=1, num_groups=1, if_act=True, name=None):
+    conv = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=(filter_size - 1) // 2,
+        groups=num_groups,
+        act=None,
+#        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name=name + '_weights'),
+        param_attr=ParamAttr(initializer=fluid.initializer.Normal(scale=0.001), learning_rate=1.0, name=name + '_weights'),
+        bias_attr=False)
+    bn_name = name + '_bn'
+    bn = fluid.layers.batch_norm(input=conv,
+                                 param_attr=ParamAttr(name=bn_name + "_scale",
+                                                      initializer=fluid.initializer.Constant(1.0)),
+                                 bias_attr=ParamAttr(name=bn_name + "_offset",
+                                                     initializer=fluid.initializer.Constant(0.0)),
+                                 moving_mean_name=bn_name + '_mean',
+                                 moving_variance_name=bn_name + '_variance')
+    if if_act:
+        bn = fluid.layers.relu(bn)
+    return bn
+
+def basic_block(input, num_filters, stride=1, downsample=False, name=None):
+    residual = input
+    conv = conv_bn_layer(input=input, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv1')
+    conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, if_act=False, name=name + '_conv2')
+    if downsample:
+        residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, if_act=False,
+                                      name=name + '_downsample')
+    return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
+
+def bottleneck_block(input, num_filters, stride=1, downsample=False, name=None):
+    residual = input
+    conv = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, name=name + '_conv1')
+    conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv2')
+    conv = conv_bn_layer(input=conv, filter_size=1, num_filters=num_filters * 4, if_act=False,
+                              name=name + '_conv3')
+    if downsample:
+        residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters * 4, if_act=False,
+                                      name=name + '_downsample')
+    return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
+
+def fuse_layers(x, channels, multi_scale_output=True, name=None):
+    out = []
+    for i in range(len(channels) if multi_scale_output else 1):
+        residual = x[i]
+        shape = residual.shape
+        width = shape[-1]
+        height = shape[-2]
+        for j in range(len(channels)):
+            if j > i:
+                y = conv_bn_layer(x[j], filter_size=1, num_filters=channels[i], if_act=False,
+                                       name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
+                y = fluid.layers.resize_bilinear(input=y, out_shape=[height, width])
+                residual = fluid.layers.elementwise_add(x=residual, y=y, act=None)
+            elif j < i:
+                y = x[j]
+                for k in range(i - j):
+                    if k == i - j - 1:
+                        y = conv_bn_layer(y, filter_size=3, num_filters=channels[i], stride=2, if_act=False,
+                                               name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))
+                    else:
+                        y = conv_bn_layer(y, filter_size=3, num_filters=channels[j], stride=2,
+                                               name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))
+                residual = fluid.layers.elementwise_add(x=residual, y=y, act=None)
+
+        residual = fluid.layers.relu(residual)
+        out.append(residual)
+    return out
+
+def branches(x, block_num, channels, name=None):
+    out = []
+    for i in range(len(channels)):
+        residual = x[i]
+        for j in range(block_num):
+            residual = basic_block(residual, channels[i],
+                                        name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))
+        out.append(residual)
+    return out
+
+def high_resolution_module(x, channels, multi_scale_output=True, name=None):
+    residual = branches(x, 4, channels, name=name)
+    out = fuse_layers(residual, channels, multi_scale_output=multi_scale_output, name=name)
+    return out
+
+def transition_layer(x, in_channels, out_channels, name=None):
+    num_in = len(in_channels)
+    num_out = len(out_channels)
+    out = []
+    for i in range(num_out):
+        if i < num_in:
+            if in_channels[i] != out_channels[i]:
+                residual = conv_bn_layer(x[i], filter_size=3, num_filters=out_channels[i],
+                                              name=name + '_layer_' + str(i + 1))
+                out.append(residual)
+            else:
+                out.append(x[i])
+        else:
+            residual = conv_bn_layer(x[-1], filter_size=3, num_filters=out_channels[i], stride=2,
+                                          name=name + '_layer_' + str(i + 1))
+            out.append(residual)
+    return out
+
+def stage(x, num_modules, channels, multi_scale_output=True, name=None):
+    out = x
+    for i in range(num_modules):
+        if i == num_modules - 1 and multi_scale_output == False:
+            out = high_resolution_module(out, channels, multi_scale_output=False, name=name + '_' + str(i + 1))
+        else:
+            out = high_resolution_module(out, channels, name=name + '_' + str(i + 1))
+
+    return out
+
+def layer1(input, name=None):
+    conv = input
+    for i in range(4):
+        conv = bottleneck_block(conv, num_filters=64, downsample=True if i == 0 else False,
+                                     name=name + '_' + str(i + 1))
+    return conv
+
+def aux_head(input, last_inp_channels, num_classes):
+    x = conv_bn_layer(input=input, filter_size=1, num_filters=last_inp_channels, stride=1, padding=0, name='aux_head_conv1')
+    x = fluid.layers.conv2d(
+        input=x,
+        num_filters=num_classes,
+        filter_size=1,
+        stride=1,
+        padding=0,
+        act=None,
+#        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name='aux_head_conv2_weights'),
+        param_attr=ParamAttr(initializer=fluid.initializer.Normal(scale=0.001), learning_rate=1.0, name='aux_head_conv2_weights'),
+        bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0), name="aux_head_conv2_bias")
+    )
+    return x
+
+def conv3x3_ocr(input, ocr_mid_channels):
+    x = conv_bn_layer(input=input, filter_size=3, num_filters=ocr_mid_channels, stride=1, padding=1, name='conv3x3_ocr')
+    return x
+
+def f_pixel(input, key_channels):
+    x = conv_bn_layer(input=input, filter_size=1, num_filters=key_channels, stride=1, padding=0, name='f_pixel_conv1')
+    x = conv_bn_layer(input=x, filter_size=1, num_filters=key_channels, stride=1, padding=0, name='f_pixel_conv2')
+    return x
+
+def f_object(input, key_channels):
+    x = conv_bn_layer(input=input, filter_size=1, num_filters=key_channels, stride=1, padding=0, name='f_object_conv1')
+    x = conv_bn_layer(input=x, filter_size=1, num_filters=key_channels, stride=1, padding=0, name='f_object_conv2')
+    return x 
+
+def f_down(input, key_channels):
+    x = conv_bn_layer(input=input, filter_size=1, num_filters=key_channels, stride=1, padding=0, name='f_down_conv')
+    return x
+
+def f_up(input, in_channels):
+    x = conv_bn_layer(input=input, filter_size=1, num_filters=in_channels, stride=1, padding=0, name='f_up_conv')
+    return x
+
+def object_context_block(x, proxy, in_channels, key_channels, scale):
+    batch_size, _, h, w = x.shape
+    if scale > 1:
+        x = fluid.layers.pool2d(x, pool_size=[scale, scale], pool_type='max')
+
+    query = f_pixel(x, key_channels)
+    query = fluid.layers.reshape(query, shape=[batch_size, key_channels, query.shape[2]*query.shape[3]])
+    query = fluid.layers.transpose(query, perm=[0, 2, 1])
+
+    key = f_object(proxy, key_channels)
+    key = fluid.layers.reshape(key, shape=[batch_size, key_channels, key.shape[2]*key.shape[3]])
+    
+    value = f_down(proxy, key_channels)
+    value = fluid.layers.reshape(value, shape=[batch_size, key_channels, value.shape[2]*value.shape[3]])
+    value = fluid.layers.transpose(value, perm=[0, 2, 1])
+
+    sim_map = fluid.layers.matmul(query, key)
+    sim_map = (key_channels**-.5) * sim_map
+    sim_map = fluid.layers.softmax(sim_map, axis=-1)
+
+    context = fluid.layers.matmul(sim_map, value)
+    context = fluid.layers.transpose(context, perm=[0, 2, 1])
+    context = fluid.layers.reshape(context, shape=[batch_size, key_channels, x.shape[2], x.shape[3]])
+    context = f_up(context, in_channels)
+
+    if scale > 1:
+        context = fluid.layers.resize_bilinear(context, out_shape=[h, w])
+
+    return context
+
+def ocr_gather_head(feats, probs, scale=1):
+    feats = fluid.layers.reshape(feats, shape=[feats.shape[0], feats.shape[1], feats.shape[2]*feats.shape[3]])
+    feats = fluid.layers.transpose(feats, perm=[0, 2, 1])
+    probs = fluid.layers.reshape(probs, shape=[probs.shape[0], probs.shape[1], probs.shape[2]*probs.shape[3]])
+    probs = fluid.layers.softmax(scale * probs, axis=2)
+    ocr_context = fluid.layers.matmul(probs, feats)
+    ocr_context = fluid.layers.transpose(ocr_context, perm=[0, 2, 1])
+    ocr_context = fluid.layers.unsqueeze(ocr_context, axes=[3])
+    return ocr_context 
+
+def ocr_distri_head(feats, proxy_feats, ocr_mid_channels, ocr_key_channels, scale=1, dropout=0.05):
+    context = object_context_block(feats, proxy_feats, ocr_mid_channels, ocr_key_channels, scale)
+    x = fluid.layers.concat([context, feats], axis=1)
+    x = conv_bn_layer(input=x, filter_size=1, num_filters=ocr_mid_channels, stride=1, padding=0, name='spatial_ocr_conv')
+    x = fluid.layers.dropout(x, dropout_prob=dropout)
+    return x
+
+def cls_head(input, num_classes):
+    x = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_classes,
+        filter_size=1,
+        stride=1,
+        padding=0,
+        act=None,
+#        param_attr=ParamAttr(initializer=MSRA(), learning_rate=1.0, name='cls_head_conv_weights'),
+        param_attr=ParamAttr(initializer=fluid.initializer.Normal(scale=0.001), learning_rate=1.0, name='cls_head_conv_weights'),
+        bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0), name="cls_head_conv_bias")
+    )
+    return x
+
+def ocr_module(input, last_inp_channels, num_classes, ocr_mid_channels, ocr_key_channels):
+    out_aux = aux_head(input, last_inp_channels, num_classes)
+    feats = conv3x3_ocr(input, ocr_mid_channels)
+    context = ocr_gather_head(feats, out_aux)
+    feats = ocr_distri_head(feats, context, ocr_mid_channels, ocr_key_channels)
+    out = cls_head(feats, num_classes)
+    return out, out_aux 
+
+def high_resolution_ocr_net(input, num_classes):
+    
+    channels_2 = cfg.MODEL.HRNET.STAGE2.NUM_CHANNELS
+    channels_3 = cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS
+    channels_4 = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS
+    
+    num_modules_2 = cfg.MODEL.HRNET.STAGE2.NUM_MODULES
+    num_modules_3 = cfg.MODEL.HRNET.STAGE3.NUM_MODULES
+    num_modules_4 = cfg.MODEL.HRNET.STAGE4.NUM_MODULES
+
+    ocr_mid_channels = cfg.MODEL.OCR.OCR_MID_CHANNELS
+    ocr_key_channels = cfg.MODEL.OCR.OCR_KEY_CHANNELS
+
+    last_inp_channels = sum(channels_4)
+
+    x = conv_bn_layer(input=input, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_1')
+    x = conv_bn_layer(input=x, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_2')
+
+    la1 = layer1(x, name='layer2')
+    tr1 = transition_layer([la1], [256], channels_2, name='tr1')
+    st2 = stage(tr1, num_modules_2, channels_2, name='st2')
+    tr2 = transition_layer(st2, channels_2, channels_3, name='tr2')
+    st3 = stage(tr2, num_modules_3, channels_3, name='st3')
+    tr3 = transition_layer(st3, channels_3, channels_4, name='tr3')
+    st4 = stage(tr3, num_modules_4, channels_4, name='st4')
+
+    # upsample
+    shape = st4[0].shape
+    height, width = shape[-2], shape[-1]
+    st4[1] = fluid.layers.resize_bilinear(
+        st4[1], out_shape=[height, width])
+    st4[2] = fluid.layers.resize_bilinear(
+        st4[2], out_shape=[height, width])
+    st4[3] = fluid.layers.resize_bilinear(
+        st4[3], out_shape=[height, width])
+
+    feats = fluid.layers.concat(st4, axis=1)
+
+    out, out_aux = ocr_module(feats, last_inp_channels, num_classes, ocr_mid_channels, ocr_key_channels)
+
+    out = fluid.layers.resize_bilinear(out, input.shape[2:])
+    out_aux = fluid.layers.resize_bilinear(out_aux, input.shape[2:])
+
+    return out, out_aux
+
+def ocnet(input, num_classes):
+    logit = high_resolution_ocr_net(input, num_classes)
+    return logit
+
+if __name__ == '__main__':
+    image_shape = [-1, 3, 769, 769]
+    image = fluid.data(name='image', shape=image_shape, dtype='float32')
+    logit = ocnet(image, 4)
+    print("logit:", logit.shape)