--- /dev/null
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import, print_function, division
+
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+from libs.configs import cfgs
+from tensorflow.contrib.slim.nets import resnet_v1
+from tensorflow.contrib.slim.nets import resnet_utils
+from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
+import tfplot as tfp
+
+
+def resnet_arg_scope(
+ is_training=True, weight_decay=cfgs.WEIGHT_DECAY, batch_norm_decay=0.997,
+ batch_norm_epsilon=1e-5, batch_norm_scale=True):
+ '''
+
+ In Default, we do not use BN to train resnet, since batch_size is too small.
+ So is_training is False and trainable is False in the batch_norm params.
+
+ '''
+ batch_norm_params = {
+ 'is_training': False, 'decay': batch_norm_decay,
+ 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale,
+ 'trainable': False,
+ 'updates_collections': tf.GraphKeys.UPDATE_OPS
+ }
+
+ with slim.arg_scope(
+ [slim.conv2d],
+ weights_regularizer=slim.l2_regularizer(weight_decay),
+ weights_initializer=slim.variance_scaling_initializer(),
+ trainable=is_training,
+ activation_fn=tf.nn.relu,
+ normalizer_fn=slim.batch_norm,
+ normalizer_params=batch_norm_params):
+ with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc:
+ return arg_sc
+
+
+def fusion_two_layer(C_i, P_j, scope):
+ '''
+ i = j+1
+ :param C_i: shape is [1, h, w, c]
+ :param P_j: shape is [1, h/2, w/2, 256]
+ :return:
+ P_i
+ '''
+ with tf.variable_scope(scope):
+ level_name = scope.split('_')[1]
+ h, w = tf.shape(C_i)[1], tf.shape(C_i)[2]
+ upsample_p = tf.image.resize_bilinear(P_j,
+ size=[h, w],
+ name='up_sample_'+level_name)
+
+ reduce_dim_c = slim.conv2d(C_i,
+ num_outputs=256,
+ kernel_size=[1, 1], stride=1,
+ scope='reduce_dim_'+level_name)
+
+ add_f = 0.5*upsample_p + 0.5*reduce_dim_c
+
+ # P_i = slim.conv2d(add_f,
+ # num_outputs=256, kernel_size=[3, 3], stride=1,
+ # padding='SAME',
+ # scope='fusion_'+level_name)
+ return add_f
+
+
+def add_heatmap(feature_maps, name):
+ '''
+
+ :param feature_maps:[B, H, W, C]
+ :return:
+ '''
+
+ def figure_attention(activation):
+ fig, ax = tfp.subplots()
+ im = ax.imshow(activation, cmap='jet')
+ fig.colorbar(im)
+ return fig
+
+ heatmap = tf.reduce_sum(feature_maps, axis=-1)
+ heatmap = tf.squeeze(heatmap, axis=0)
+ tfp.summary.plot(name, figure_attention, [heatmap])
+
+
+def resnet_base(img_batch, scope_name, is_training=True):
+ '''
+ this code is derived from light-head rcnn.
+ https://github.com/zengarden/light_head_rcnn
+
+ It is convenient to freeze blocks. So we adapt this mode.
+ '''
+ if scope_name == 'resnet_v1_50':
+ middle_num_units = 6
+ elif scope_name == 'resnet_v1_101':
+ middle_num_units = 23
+ else:
+ raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....yjr')
+
+ blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
+ resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
+ resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=2),
+ resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]
+ # when use fpn . stride list is [1, 2, 2]
+
+ with slim.arg_scope(resnet_arg_scope(is_training=False)):
+ with tf.variable_scope(scope_name, scope_name):
+ # Do the first few layers manually, because 'SAME' padding can behave inconsistently
+ # for images of different sizes: sometimes 0, sometimes 1
+ net = resnet_utils.conv2d_same(
+ img_batch, 64, 7, stride=2, scope='conv1')
+ net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
+ net = slim.max_pool2d(
+ net, [3, 3], stride=2, padding='VALID', scope='pool1')
+
+ not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True]
+ # Fixed_Blocks can be 1~3
+
+ with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))):
+ C2, end_points_C2 = resnet_v1.resnet_v1(net,
+ blocks[0:1],
+ global_pool=False,
+ include_root_block=False,
+ scope=scope_name)
+
+ # C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape')
+ add_heatmap(C2, name='Layer2/C2_heat')
+
+ with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))):
+ C3, end_points_C3 = resnet_v1.resnet_v1(C2,
+ blocks[1:2],
+ global_pool=False,
+ include_root_block=False,
+ scope=scope_name)
+
+ # C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape')
+ add_heatmap(C3, name='Layer3/C3_heat')
+ with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))):
+ C4, end_points_C4 = resnet_v1.resnet_v1(C3,
+ blocks[2:3],
+ global_pool=False,
+ include_root_block=False,
+ scope=scope_name)
+
+ add_heatmap(C4, name='Layer4/C4_heat')
+
+ # C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape')
+ with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
+ C5, end_points_C5 = resnet_v1.resnet_v1(C4,
+ blocks[3:4],
+ global_pool=False,
+ include_root_block=False,
+ scope=scope_name)
+ # C5 = tf.Print(C5, [tf.shape(C5)], summarize=10, message='C5_shape')
+ add_heatmap(C5, name='Layer5/C5_heat')
+
+ feature_dict = {'C2': end_points_C2['{}/block1/unit_2/bottleneck_v1'.format(scope_name)],
+ 'C3': end_points_C3['{}/block2/unit_3/bottleneck_v1'.format(scope_name)],
+ 'C4': end_points_C4['{}/block3/unit_{}/bottleneck_v1'.format(scope_name, middle_num_units - 1)],
+ 'C5': end_points_C5['{}/block4/unit_3/bottleneck_v1'.format(scope_name)],
+ # 'C5': end_points_C5['{}/block4'.format(scope_name)],
+ }
+
+ # feature_dict = {'C2': C2,
+ # 'C3': C3,
+ # 'C4': C4,
+ # 'C5': C5}
+
+ pyramid_dict = {}
+ with tf.variable_scope('build_pyramid'):
+ with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY),
+ activation_fn=None, normalizer_fn=None):
+
+ P5 = slim.conv2d(C5,
+ num_outputs=256,
+ kernel_size=[1, 1],
+ stride=1, scope='build_P5')
+ if "P6" in cfgs.LEVLES:
+ P6 = slim.max_pool2d(P5, kernel_size=[1, 1], stride=2, scope='build_P6')
+ pyramid_dict['P6'] = P6
+
+ pyramid_dict['P5'] = P5
+
+ for level in range(4, 1, -1): # build [P4, P3, P2]
+
+ pyramid_dict['P%d' % level] = fusion_two_layer(C_i=feature_dict["C%d" % level],
+ P_j=pyramid_dict["P%d" % (level+1)],
+ scope='build_P%d' % level)
+ for level in range(4, 1, -1):
+ pyramid_dict['P%d' % level] = slim.conv2d(pyramid_dict['P%d' % level],
+ num_outputs=256, kernel_size=[3, 3], padding="SAME",
+ stride=1, scope="fuse_P%d" % level)
+ for level in range(5, 1, -1):
+ add_heatmap(pyramid_dict['P%d' % level], name='Layer%d/P%d_heat' % (level, level))
+
+ # return [P2, P3, P4, P5, P6]
+ print("we are in Pyramid::-======>>>>")
+ print(cfgs.LEVLES)
+ print("base_anchor_size are: ", cfgs.BASE_ANCHOR_SIZE_LIST)
+ print(20 * "__")
+ return [pyramid_dict[level_name] for level_name in cfgs.LEVLES]
+ # return pyramid_dict # return the dict. And get each level by key. But ensure the levels are consitant
+ # return list rather than dict, to avoid dict is unordered
+
+
+
+def restnet_head(inputs, is_training, scope_name):
+ '''
+
+ :param inputs: [minibatch_size, 7, 7, 256]
+ :param is_training:
+ :param scope_name:
+ :return:
+ '''
+
+ with tf.variable_scope('build_fc_layers'):
+
+ # fc1 = slim.conv2d(inputs=inputs,
+ # num_outputs=1024,
+ # kernel_size=[7, 7],
+ # padding='VALID',
+ # scope='fc1') # shape is [minibatch_size, 1, 1, 1024]
+ # fc1 = tf.squeeze(fc1, [1, 2], name='squeeze_fc1')
+
+ inputs = slim.flatten(inputs=inputs, scope='flatten_inputs')
+
+ fc1 = slim.fully_connected(inputs, num_outputs=1024, scope='fc1')
+
+ fc2 = slim.fully_connected(fc1, num_outputs=1024, scope='fc2')
+
+ # fc3 = slim.fully_connected(fc2, num_outputs=1024, scope='fc3')
+
+ # we add fc3 to increase the ability of fast-rcnn head
+ return fc2
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Code Review / ealt-edge.git / blobdiff