Last change
on this file since 16289 was
16289,
checked in by dferreira, 3 years ago

Initial content. All the tests done to three opensource platrofms.

File size:
4.0 KB

Line  

1  name: "CaffeNet" 

2  input: "data" 

3  input_dim: 50 

4  input_dim: 3 

5  input_dim: 224 

6  input_dim: 224 

7  layer { 

8  name: "conv1_1" 

9  type: "Convolution" 

10  bottom: "data" 

11  top: "conv1_1" 

12  convolution_param { 

13  num_output: 64 

14  kernel_size: 3 

15  stride: 1 

16  pad: 1 

17  } 

18  } 

19  layer { 

20  name: "relu1_1" 

21  type: "ReLU" 

22  bottom: "conv1_1" 

23  top: "conv1_1" 

24  } 

25  layer { 

26  name: "pool1" 

27  type: "Pooling" 

28  bottom: "conv1_1" 

29  top: "pool1" 

30  pooling_param { 

31  pool: MAX 

32  kernel_size: 2 

33  stride: 2 

34  } 

35  } 

36  layer { 

37  name: "conv2_1" 

38  type: "Convolution" 

39  bottom: "pool1" 

40  top: "conv2_1" 

41  convolution_param { 

42  num_output: 128 

43  kernel_size: 3 

44  stride: 1 

45  pad: 1 

46  } 

47  } 

48  layer { 

49  name: "relu2_1" 

50  type: "ReLU" 

51  bottom: "conv2_1" 

52  top: "conv2_1" 

53  } 

54  layer { 

55  name: "pool2" 

56  type: "Pooling" 

57  bottom: "conv2_1" 

58  top: "pool2" 

59  pooling_param { 

60  pool: MAX 

61  kernel_size: 2 

62  stride: 2 

63  } 

64  } 

65  layer { 

66  name: "conv3_1" 

67  type: "Convolution" 

68  bottom: "pool2" 

69  top: "conv3_1" 

70  convolution_param { 

71  num_output: 256 

72  kernel_size: 3 

73  stride: 1 

74  pad: 1 

75  } 

76  } 

77  layer { 

78  name: "relu3_1" 

79  type: "ReLU" 

80  bottom: "conv3_1" 

81  top: "conv3_1" 

82  } 

83  layer { 

84  name: "conv3_2" 

85  type: "Convolution" 

86  bottom: "conv3_1" 

87  top: "conv3_2" 

88  convolution_param { 

89  num_output: 256 

90  kernel_size: 3 

91  stride: 1 

92  pad: 1 

93  } 

94  } 

95  layer { 

96  name: "relu3_2" 

97  type: "ReLU" 

98  bottom: "conv3_2" 

99  top: "conv3_2" 

100  } 

101  layer { 

102  name: "pool3" 

103  type: "Pooling" 

104  bottom: "conv3_2" 

105  top: "pool3" 

106  pooling_param { 

107  pool: MAX 

108  kernel_size: 2 

109  stride: 2 

110  } 

111  } 

112  layer { 

113  name: "conv4_1" 

114  type: "Convolution" 

115  bottom: "pool3" 

116  top: "conv4_1" 

117  convolution_param { 

118  num_output: 512 

119  kernel_size: 3 

120  stride: 1 

121  pad: 1 

122  } 

123  } 

124  layer { 

125  name: "relu4_1" 

126  type: "ReLU" 

127  bottom: "conv4_1" 

128  top: "conv4_1" 

129  } 

130  layer { 

131  name: "conv4_2" 

132  type: "Convolution" 

133  bottom: "conv4_1" 

134  top: "conv4_2" 

135  convolution_param { 

136  num_output: 512 

137  kernel_size: 3 

138  stride: 1 

139  pad: 1 

140  } 

141  } 

142  layer { 

143  name: "relu4_2" 

144  type: "ReLU" 

145  bottom: "conv4_2" 

146  top: "conv4_2" 

147  } 

148  layer { 

149  name: "pool4" 

150  type: "Pooling" 

151  bottom: "conv4_2" 

152  top: "pool4" 

153  pooling_param { 

154  pool: MAX 

155  kernel_size: 2 

156  stride: 2 

157  } 

158  } 

159  layer { 

160  name: "conv5_1" 

161  type: "Convolution" 

162  bottom: "pool4" 

163  top: "conv5_1" 

164  convolution_param { 

165  num_output: 512 

166  kernel_size: 3 

167  stride: 1 

168  pad: 1 

169  } 

170  } 

171  layer { 

172  name: "relu5_1" 

173  type: "ReLU" 

174  bottom: "conv5_1" 

175  top: "conv5_1" 

176  } 

177  layer { 

178  name: "conv5_2" 

179  type: "Convolution" 

180  bottom: "conv5_1" 

181  top: "conv5_2" 

182  convolution_param { 

183  num_output: 512 

184  kernel_size: 3 

185  stride: 1 

186  pad: 1 

187  } 

188  } 

189  layer { 

190  name: "relu5_2" 

191  type: "ReLU" 

192  bottom: "conv5_2" 

193  top: "conv5_2" 

194  } 

195  layer { 

196  name: "pool5" 

197  type: "Pooling" 

198  bottom: "conv5_2" 

199  top: "pool5" 

200  pooling_param { 

201  pool: MAX 

202  kernel_size: 2 

203  stride: 2 

204  } 

205  } 

206  layer { 

207  name: "conv6_1" 

208  type: "Convolution" 

209  bottom: "pool5" 

210  top: "conv6_1" 

211  convolution_param { 

212  num_output: 1024 

213  kernel_size: 3 

214  stride: 1 

215  pad: 1 

216  } 

217  } 

218  layer { 

219  name: "relu6_1" 

220  type: "ReLU" 

221  bottom: "conv6_1" 

222  top: "conv6_1" 

223  } 

224  layer { 

225  name: "conv6_2" 

226  type: "Convolution" 

227  bottom: "conv6_1" 

228  top: "conv6_2" 

229  convolution_param { 

230  num_output: 1024 

231  kernel_size: 3 

232  stride: 1 

233  pad: 1 

234  } 

235  } 

236  layer { 

237  name: "relu6_2" 

238  type: "ReLU" 

239  bottom: "conv6_2" 

240  top: "conv6_2" 

241  } 

242  layer { 

243  name: "conv6_3" 

244  type: "Convolution" 

245  bottom: "conv6_2" 

246  top: "conv6_3" 

247  convolution_param { 

248  num_output: 1024 

249  kernel_size: 3 

250  stride: 1 

251  pad: 1 

252  } 

253  } 

254  layer { 

255  name: "relu6_3" 

256  type: "ReLU" 

257  bottom: "conv6_3" 

258  top: "conv6_3" 

259  } 

260  layer { 

261  name: "conv6_4" 

262  type: "Convolution" 

263  bottom: "conv6_3" 

264  top: "conv6_4" 

265  convolution_param { 

266  num_output: 1539 

267  kernel_size: 3 

268  stride: 1 

269  pad: 1 

270  } 

271  } 

272  layer { 

273  name: "pool6" 

274  type: "Pooling" 

275  bottom: "conv6_4" 

276  top: "pool6" 

277  pooling_param { 

278  pool: AVE 

279  kernel_size: 7 

280  stride: 1 

281  } 

282  } 

283  layer { 

284  name: "prob" 

285  type: "Sigmoid" 

286  bottom: "pool6" 

287  top: "prob" 

288  } 

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