(net, mode)
| 101 | |
| 102 | |
| 103 | def check_kernels(net, mode): |
| 104 | # Check the Encoder blocks |
| 105 | for layer_num, res_block in enumerate(net.res_blocks): |
| 106 | for inner_num, layer in enumerate(res_block.layers): |
| 107 | if layer_num > 0 and inner_num == 0: |
| 108 | sz = 2 |
| 109 | else: |
| 110 | sz = 1 |
| 111 | |
| 112 | if ( |
| 113 | layer.layers.conv1.kernel_size != (1, 1) |
| 114 | or layer.layers.conv2.kernel_size != (3, 3) |
| 115 | or layer.layers.conv3.kernel_size != (1, 1) |
| 116 | ): |
| 117 | return True |
| 118 | |
| 119 | if ( |
| 120 | layer.layers.conv1.stride != (1, 1) |
| 121 | or layer.layers.conv2.stride != (sz, sz) |
| 122 | or layer.layers.conv3.stride != (1, 1) |
| 123 | ): |
| 124 | return True |
| 125 | |
| 126 | sz2 = 1 |
| 127 | if layer_num > 0: |
| 128 | sz2 = 2 |
| 129 | if res_block.shortcut.kernel_size != (1, 1) or res_block.shortcut.stride != (sz2, sz2): |
| 130 | return True |
| 131 | |
| 132 | if net.bottleneck.conv_bottleneck.kernel_size != (1, 1) or net.bottleneck.conv_bottleneck.stride != (1, 1): |
| 133 | return True |
| 134 | |
| 135 | # Check HV Branch |
| 136 | if check_branch(net.horizontal_vertical.decoder_blocks, mode): |
| 137 | return True |
| 138 | if check_output(net.horizontal_vertical.output_features, mode): |
| 139 | return True |
| 140 | |
| 141 | # Check NP Branch |
| 142 | if check_branch(net.nucleus_prediction.decoder_blocks, mode): |
| 143 | return True |
| 144 | if check_output(net.nucleus_prediction.output_features, mode): |
| 145 | return True |
| 146 | |
| 147 | # Check NC Branch |
| 148 | if check_branch(net.type_prediction.decoder_blocks, mode): |
| 149 | return True |
| 150 | if check_output(net.type_prediction.output_features, mode): |
| 151 | return True |
| 152 | |
| 153 | return False |
| 154 | |
| 155 | |
| 156 | class TestHoverNet(unittest.TestCase): |
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