def res_layer(inp, chl, stride = 1, proj = False): pre = inp inp = conv_bn(inp, 1, stride, 0, chl // 4, True) chl //= 4 name = inp.name #Global Average Pooling SE = inp.mean(axis = 3).mean(axis = 2) sum_lay = 0 out_lay = 0 width = 4 lay = FullyConnected( "fc0({})".format(name), SE, output_dim = chl, nonlinearity = ReLU() ) #fc1 lay = FullyConnected( "fc1({})".format(name), lay, output_dim = chl * width, nonlinearity = Identity() ) lay = lay.reshape(inp.shape[0], chl, width) lay = Softmax("softmax({})".format(name), lay, axis = 2) for i in range(width): if i == 0: inp_lay = inp else: inp_lay = O.Concat([inp[:, width:, :, :], inp[:, :width, :, :]], axis = 1) inp_lay = inp_lay * lay[:, :, i].dimshuffle(0, 1, 'x', 'x') inp = inp_lay chl *= 4 inp = conv_bn(inp, 3, 1, 1, chl // 4, True) inp = conv_bn(inp, 1, 1, 0, chl, False) if proj: pre = conv_bn(pre, 1, stride, 0, chl, False) name = inp.name #Global Average Pooling SE = inp.mean(axis = 3).mean(axis = 2) sum_lay = 0 out_lay = 0 width = 4 lay = FullyConnected( "fc0({})".format(name), SE, output_dim = chl, nonlinearity = ReLU() ) #fc1 lay = FullyConnected( "fc1({})".format(name), lay, output_dim = chl * width, nonlinearity = Identity() ) lay = lay.reshape(inp.shape[0], chl, width) lay = Softmax("softmax({})".format(name), lay, axis = 2) for i in range(width): if i == 0: inp_lay = inp else: inp_lay = O.Concat([inp[:, width:, :, :], inp[:, :width, :, :]], axis = 1) inp_lay = inp_lay * lay[:, :, i].dimshuffle(0, 1, 'x', 'x') inp = inp_lay inp = arith.ReLU(inp + pre) return inp
def make_network(minibatch_size=64): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) k, l = 20, (40 - 4) // 3 lay = bn_relu_conv(inp, 3, 1, 1, k, False, False) for i in range(3): lay = transition(dense_block(lay, k, l), i) #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) info = CInfo() info.get_complexity(network.outputs).as_table().show() return network
def make_network(minibatch_size=64): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) k, l = 24, (100 - 4) // 3 for i in range(3): lay = transition(dense_block(lay, k, l, False), i) feature = lay pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size=128, debug=False): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size), dtype=np.float32) label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32) lay = conv_bn(inp, 3, 1, 1, 16, True) n = 4 lis = [16 * 4, 32 * 4, 64 * 4] for i in range(len(lis)): lay = res_block(lay, lis[i], i, n) fc = attentional_active_pooling(lay, 10) pred = Softmax("pred", fc) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) """ if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() """ return network
def make_network(minibatch_size=128): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 15, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) #lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) lay, conv = conv_bn(inp, 3, 1, 1, 16, True) out = [conv] for chl in [32, 64, 128]: for i in range(10): lay, conv = conv_bn(lay, 3, 1, 1, chl, True) out.append(conv) if chl != 128: lay = b_resize("pooling{}".format(chl), lay) lay = Pooling2D("pooling{}".format(chl), lay, window=2, mode="MAX") #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred] + out) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size=128): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) lay = conv_bn(inp, 3, 1, 1, 16, True) n = 3 lis = [16, 32, 64] for i in lis: lay = res_block(lay, i, n) #global average pooling feature = lay.mean(axis=2).mean(axis=2) pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(2 / 64)**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size=64): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) k, l = 12, (40 - 4) // 3 for i in range(3): lay = transition(dense_block(lay, k, l), i) #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size = 128, debug = False): patch_size = 32 inp = DataProvider("data", shape = (minibatch_size, 3, patch_size, patch_size), dtype = np.float32) label = DataProvider("label", shape = (minibatch_size, ), dtype = np.int32) lay = conv_bn(inp, 3, 1, 1, 16, True) n = 18 lis = [16, 32, 64] for i in lis: lay = res_block(lay, i, n) #global average pooling #feature = lay.mean(axis = 2).mean(axis = 2) feature = Pooling2D("pooling", lay, window = 8, stride = 8, padding = 0, mode = "AVERAGE") pred = Softmax("pred", FullyConnected( "fc0", feature, output_dim = 10, nonlinearity = Identity() )) network = Network(outputs = [pred]) network.loss_var = CrossEntropyLoss(pred, label) if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() return network
def make_network(minibatch_size = 128): patch_size = 32 inp = DataProvider("data", shape = (minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape = (minibatch_size, )) #lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) lay, conv = conv_bn(inp, 3, 1, 1, 16, True) out = [conv] for chl in [32 * 3, 64 * 3, 128 * 3]: for i in range(10): lay, conv1, conv2 = xcep_layer(lay, chl) out.append(conv1) out.append(conv2) if chl != 128 * 3: lay = Pooling2D("pooling{}".format(chl), lay, window = 2, mode = "MAX") #global average pooling print(lay.partial_shape) feature = lay.mean(axis = 2).mean(axis = 2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") W = ortho_group.rvs(feature.partial_shape[1]) W = W[:, :10] W = ConstProvider(W) b = ConstProvider(np.zeros((10, ))) pred = Softmax("pred", FullyConnected( "fc0", feature, output_dim = 10, W = W, b = b, nonlinearity = Identity() )) network = Network(outputs = [pred] + out) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size=128, debug=False): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size), dtype=np.float32) label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32) lay, w = conv_bn(inp, 3, 1, 1, 16, True) lis_w = [w] n = 3 lis = [16, 32, 64] for i in lis: lay, lis_new = res_block(lay, i, n) lis_w += lis_new #global average pooling #feature = lay.mean(axis = 2).mean(axis = 2) feature = Pooling2D("pooling", lay, window=8, stride=8, padding=0, mode="AVERAGE") pred = Softmax( "pred", FullyConnected( "fc0", feature, output_dim=10, #W = G(mean = 0, std = (1 / 64)**0.5), #b = C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) lmd = 1 for w in lis_w: w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0) w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0) A = O.MatMul(w.dimshuffle(1, 0), w) network.loss_var += lmd * ( (A - np.identity(A.partial_shape[0]))**2).mean() if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() return network
def make_network(minibatch_size=128, debug=False): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size), dtype=np.float32) label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32) lay = conv_bn(inp, 3, 1, 1, 16 * 4 * 2, True) n = 4 * 3 group = 8 lis = [16 * 4, 32 * 4, 64 * 4] for i in range(len(lis)): lay = res_block(lay, lis[i], i, n, group) #global average pooling #feature = lay.mean(axis = 2).mean(axis = 2) feature = Pooling2D("pooling", lay, window=8, stride=8, padding=0, mode="AVERAGE") pred = Softmax( "pred", FullyConnected( "fc0", feature, output_dim=10, #W = G(mean = 0, std = (1 / 64)**0.5), #b = C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) info = CInfo() info.get_complexity(network.outputs).as_table().show() """ if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() """ return network
def make_network(minibatch_size=128): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) #lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) lay, conv = conv_bn(inp, 3, 1, 1, 16, True) out = [conv] for chl in [32, 64, 128]: for i in range(10): lay, conv = conv_bn(lay, 3, 1, 1, chl, True) out.append(conv) if chl != 128: lay = Pooling2D("pooling{}".format(chl), lay, window=2, mode="MAX") #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred] + out) network.loss_var = CrossEntropyLoss(pred, label) #conv1 = out[0] #print(conv1.inputs[1].partial_shape) lmd = 0.01 for conv_lay in out: w = conv_lay #w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0) w = w.dimshuffle(1, 0, 2, 3) w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0) w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0) A = MatMul(w.dimshuffle(1, 0), w) #print(A.partial_shape) network.loss_var += lmd * ( (A - np.identity(A.partial_shape[0]))**2).sum() return network
def make_network(minibatch_size=128, debug=False): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size), dtype=np.float32) label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32) lay = conv_bn(inp, 3, 1, 1, 16, True) lis = [16, 32, 64] for i in range(len(lis)): #lay = res_block(lay, lis[i], i, n) for j in range(40): lay = conv_bn(lay, 3, 1, 1, lis[i], False) if i < len(lis) - 1: lay = conv_bn(lay, 2, 2, 0, lis[i + 1], True) #global average pooling feature = lay.mean(axis=2).mean(axis=2) pred = Softmax( "pred", FullyConnected( "fc0", feature, output_dim=10, #W = G(mean = 0, std = (1 / 64)**0.5), #b = C(0), nonlinearity=Identity())) network = Network(outputs=[pred]) #info = CInfo() #info.get_complexity(network.outputs).as_table().show() network.loss_var = CrossEntropyLoss(pred, label) """ if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() """ return network
def make_network(minibatch_size=128): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) idxmap = np.zeros((128, 3, 32, 32, 4), dtype=np.int32) sample = IndexingRemap(inp, idxmap) network = Network(outputs=[sample]) sample = FullyConnected("fc", sample, output_dim=1) network.loss_var = sample.sum() return network #lay = bn_relu_conv(inp, 3, 1, 1, 16, False, False) lay, conv = conv_bn(inp, 3, 1, 1, 32, True) out = [conv] """ for chl in [32, 64, 128]: for i in range(10): lay, conv = conv_bn(lay, 3, 1, 1, chl, True) out.append(conv) if chl != 128: lay = dfpooling("pooling{}".format(chl), lay) """ chl = 32 for i in range(3): lay, conv = dfconv(lay, chl, True, i == 0) #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, W=G(mean=0, std=(1 / feature.partial_shape[1])**0.5), b=C(0), nonlinearity=Identity())) network = Network(outputs=[pred] + out) network.loss_var = CrossEntropyLoss(pred, label) return network
def make_network(minibatch_size=64): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size)) label = DataProvider("label", shape=(minibatch_size, )) lay, w = bn_relu_conv(inp, 3, 1, 1, 16, False, False) lis_w = [w] k, l = 12, (40 - 4) // 3 for i in range(3): #lay = transition(dense_block(lay, k, l), i) lay, lis_new = dense_block(lay, k, l) lis_w += lis_new lay, lis_new = transition(lay, i) lis_w += lis_new #global average pooling print(lay.partial_shape) feature = lay.mean(axis=2).mean(axis=2) #feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE") pred = Softmax( "pred", FullyConnected("fc0", feature, output_dim=10, nonlinearity=Identity())) network = Network(outputs=[pred]) network.loss_var = CrossEntropyLoss(pred, label) lmd = 0.01 for w in lis_w: if w is None: continue print(w.partial_shape) w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0) w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0) A = O.MatMul(w.dimshuffle(1, 0), w) network.loss_var += lmd * ( (A - np.identity(A.partial_shape[0]))**2).sum() return network
def get(args): img_size = 224 num_inputs = 3 data = DataProvider('data', shape=(args.batch_size, num_inputs, img_size, img_size)) inp = data f = create_bn_relu("conv1", inp, ksize=7, stride=2, pad=3, num_outputs=64, has_relu=True, conv_name_fun=None, args=args) f = Pooling2D("pool1", f, window=3, stride=2, padding=1, mode="MAX", format=args.format) pre = [2, 3, 4, 5] stages = [3, 4, 6, 3] mid_outputs = [64, 128, 256, 512] enable_stride = [False, True, True, True] for p, s, o, es in zip(pre, stages, mid_outputs, enable_stride): for i in range(s): has_proj = False if i > 0 else True stride = 1 if not es or i > 0 else 2 prefix = "{}{}".format(p, chr(ord("a") + i)) f = create_bottleneck(prefix, f, stride, o, o * 4, args, has_proj) print("{}\t{}".format(prefix, f.partial_shape)) f = Pooling2D("pool5", f, window=7, stride=7, padding=0, mode="AVERAGE", format=args.format) f = FullyConnected("fc1000", f, output_dim=1000, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f = Softmax("cls_softmax", f) f.init_weights() net = RawNetworkBuilder(inputs=[data], outputs=[f]) return net
def make_network(): batch_size = 200 img_size = 224 data = DataProvider("data", shape=(batch_size, 3, img_size, img_size)) label = DataProvider("label", shape=(batch_size, )) f = create_conv_relu("conv1_1", data, ksize=3, stride=1, pad=1, num_outputs=64) f = create_conv_relu("conv1_2", f, ksize=3, stride=1, pad=1, num_outputs=64) f = CaffePooling2D("pool1", f, window=2, stride=2, padding=0, mode="MAX") f = create_conv_relu("conv2_1", f, ksize=3, stride=1, pad=1, num_outputs=128) f = create_conv_relu("conv2_2", f, ksize=3, stride=1, pad=1, num_outputs=128) f = CaffePooling2D("pool2", f, window=2, stride=2, padding=0, mode="MAX") f = create_conv_relu("conv3_1", f, ksize=3, stride=1, pad=1, num_outputs=256) f = create_conv_relu("conv3_2", f, ksize=3, stride=1, pad=1, num_outputs=256) f = create_conv_relu("conv3_3", f, ksize=3, stride=1, pad=1, num_outputs=256) f = CaffePooling2D("pool3", f, window=2, stride=2, padding=0, mode="MAX") f = create_conv_relu("conv4_1", f, ksize=3, stride=1, pad=1, num_outputs=512) f = create_conv_relu("conv4_2", f, ksize=3, stride=1, pad=1, num_outputs=512) f = create_conv_relu("conv4_3", f, ksize=3, stride=1, pad=1, num_outputs=512) f = CaffePooling2D("pool4", f, window=2, stride=2, padding=0, mode="MAX") f = create_conv_relu("conv5_1", f, ksize=3, stride=1, pad=1, num_outputs=512) f = create_conv_relu("conv5_2", f, ksize=3, stride=1, pad=1, num_outputs=512) f = create_conv_relu("conv5_3", f, ksize=3, stride=1, pad=1, num_outputs=512) f = CaffePooling2D("pool5", f, window=2, stride=2, padding=0, mode="MAX") f = FullyConnected("fc6", f, output_dim=4096, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f = ReLU(f) f = FullyConnected("fc7", f, output_dim=4096, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f = ReLU(f) f = FullyConnected("fc8", f, output_dim=1000, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f = Softmax("cls_softmax", f) net = RawNetworkBuilder(inputs=[data, label], outputs=[f], loss=CrossEntropyLoss(f, label)) return net
conv3 = Conv2D("conv3", pooling1, kernel_shape = 3, output_nr_channel = 10, W = G(mean = 0.0001, std = (1 / (5 * 3 * 3))**0.5), b = C(0), padding = (1, 1), nonlinearity = ReLU()) conv4 = Conv2D("conv4", conv3, kernel_shape = 3, output_nr_channel = 10, W = G(mean = 0.0001, std = (1 / (10 * 3 * 3))**0.5), b = C(0), padding = (1, 1), nonlinearity = ReLU()) pooling2 = Pooling2D("pooling2", conv4, window = (2, 2), mode = "max") feature = pooling2.reshape((-1, 7 * 7 * 10)) fc1 = FC("fc1", feature, output_dim = 100, W = G(mean = 0.0001, std = (1 / 490)**0.5), b = C(0), nonlinearity = ReLU()) fc2 = FC("fc2", fc1, output_dim = 10, W = G(mean = 0, std = (1 / 100)**0.5), b = C(0), nonlinearity = Identity()) #output_mat = Exp(fc2) / Exp(fc2).sum(axis = 1).dimshuffle(0, 'x') pred = Softmax("pred", fc2) label = DataProvider(name = "label", shape = (minibatch_size, ), dtype = np.int32) #loss = -Log(indexing_one_hot(output_mat, 1, label)).mean() loss = CrossEntropyLoss(pred, label) network = Network(pred, loss)
def make_network(minibatch_size=128, debug=False): patch_size = 32 inp = DataProvider("data", shape=(minibatch_size, 3, patch_size, patch_size), dtype=np.float32) label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32) lay = conv_bn(inp, 3, 1, 1, 16, True) lis = [16, 32, 64] for i in range(len(lis)): #lay = res_block(lay, lis[i], i, n) for j in range(10): lay = conv_bn(lay, 3, 1, 1, lis[i], True) if i < len(lis) - 1: lay = conv_bn(lay, 2, 2, 0, lis[i + 1], True) #global average pooling #feature = lay.mean(axis = 2).mean(axis = 2) #feature = Pooling2D("pooling", lay, window = 8, stride = 8, padding = 0, mode = "AVERAGE") lay = lay.reshape(lay.shape[0], lay.shape[1], lay.shape[2] * lay.shape[3]) print(lay.partial_shape) a = O.ParamProvider( "a", np.random.randn(lay.partial_shape[2], 10) * (1 / lay.partial_shape[2])**0.5) a = a.dimshuffle('x', 0, 1) a = a.broadcast( (lay.partial_shape[0], a.partial_shape[1], a.partial_shape[2])) print(a.partial_shape) b = O.ParamProvider( "b", np.random.randn(lay.partial_shape[2], 10) * (1 / lay.partial_shape[2])**0.5) b = b.dimshuffle('x', 0, 1) b = b.broadcast( (lay.partial_shape[0], b.partial_shape[1], b.partial_shape[2])) print(b.partial_shape) fca = O.BatchedMatMul(lay, a) fcb = O.BatchedMatMul(lay, b) fc = O.BatchedMatMul(fca.dimshuffle(0, 2, 1), fcb) / 64 outs = [] for i in range(10): outs.append(fc[:, i, i].dimshuffle(0, 'x')) fc = O.Concat(outs, axis=1) pred = Softmax("pred", fc) """ pred = Softmax("pred", FullyConnected( "fc0", feature, output_dim = 10, #W = G(mean = 0, std = (1 / 64)**0.5), #b = C(0), nonlinearity = Identity() )) """ network = Network(outputs=[pred]) #info = CInfo() #info.get_complexity(network.outputs).as_table().show() network.loss_var = CrossEntropyLoss(pred, label) """ if debug: visitor = NetworkVisitor(network.loss_var) for i in visitor.all_oprs: print(i) print(i.partial_shape) print("input = ", i.inputs) print("output = ", i.outputs) print() """ return network
def make_network(): batch_size = config.minibatch_size img_size = config.img_size data = DataProvider("data", shape=(batch_size, 3, img_size, img_size)) label = DataProvider("label", shape=(batch_size, 8)) f = create_bn_relu("conv1", data, ksize=3, stride=2, pad=1, num_outputs=24) f = Pooling2D("pool1", f, window=3, stride=2, padding=1, mode="MAX") pre = [2, 3, 4] stages = [4, 8, 4] mid_outputs = [32, 64, 128] enable_stride = [True, True, True] for p, s, o, es in zip(pre, stages, mid_outputs, enable_stride): for i in range(s): prefix = "{}{}".format(p, chr(ord("a") + i)) stride = 1 if not es or i > 0 else 2 has_proj = False if i > 0 else True f = create_xception(prefix, f, stride, o, o * 4, has_proj) print("{}\t{}".format(prefix, f.partial_shape)) f1 = Pooling2D("pool5_1", f, window=7, stride=7, padding=0, mode="AVERAGE") f1 = FullyConnected("fc3_1", f1, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f1 = Softmax("cls_softmax_1", f1) f2 = Pooling2D("pool5_2", f, window=7, stride=7, padding=0, mode="AVERAGE") f2 = FullyConnected("fc3_2", f2, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f2 = Softmax("cls_softmax_2", f2) f3 = Pooling2D("pool5_3", f, window=7, stride=7, padding=0, mode="AVERAGE") f3 = FullyConnected("fc3_3", f3, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f3 = Softmax("cls_softmax_3", f3) f4 = Pooling2D("pool5_4", f, window=7, stride=7, padding=0, mode="AVERAGE") f4 = FullyConnected("fc3_4", f4, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f4 = Softmax("cls_softmax_4", f4) f5 = Pooling2D("pool5_5", f, window=7, stride=7, padding=0, mode="AVERAGE") f5 = FullyConnected("fc3_5", f5, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f5 = Softmax("cls_softmax_5", f5) f6 = Pooling2D("pool5_6", f, window=7, stride=7, padding=0, mode="AVERAGE") f6 = FullyConnected("fc3_6", f6, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f6 = Softmax("cls_softmax_6", f6) f7 = Pooling2D("pool5_7", f, window=7, stride=7, padding=0, mode="AVERAGE") f7 = FullyConnected("fc3_7", f7, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f7 = Softmax("cls_softmax_7", f7) f8 = Pooling2D("pool5_8", f, window=7, stride=7, padding=0, mode="AVERAGE") f8 = FullyConnected("fc3_8", f8, output_dim=2, nonlinearity=mgsk.opr.helper.elemwise_trans.Identity()) f8 = Softmax("cls_softmax_8", f8) losses = {} # cross-entropy loss # from IPython import embed # embed() label_1 = label[:, 0] label_2 = label[:, 1] label_3 = label[:, 2] label_4 = label[:, 3] label_5 = label[:, 4] label_6 = label[:, 5] label_7 = label[:, 6] label_8 = label[:, 7] loss_xent_0 = O.cross_entropy(f1, label_1, name='loss_pose') try: loss_xent_1 = O.cross_entropy_with_mask(f2, label_2, label_1) loss_xent_2 = O.cross_entropy_with_mask(f3, label_3, label_1) loss_xent_3 = O.cross_entropy_with_mask(f4, label_4, label_1) loss_xent_4 = O.cross_entropy_with_mask(f5, label_5, label_1) loss_xent_5 = O.cross_entropy_with_mask(f6, label_6, label_1) loss_xent_6 = O.cross_entropy_with_mask(f7, label_7, label_1) loss_xent_7 = O.cross_entropy_with_mask(f8, label_8, label_1) except Exception as err: print(err) loss_xent = loss_xent_0 + loss_xent_1 + loss_xent_2 + loss_xent_3 + loss_xent_4 + loss_xent_5 + loss_xent_6 + loss_xent_7 losses['loss_xent'] = loss_xent # weight decay regularization loss loss_weight_decay = 0 if config.weight_decay: weight_decay = config.weight_decay with GroupNode('weight_decay').context_reg(): for opr in iter_dep_opr(loss_xent): if not isinstance(opr, ParamProvider) or opr.freezed: continue param = opr name = param.name if not (name.endswith('W')): continue # logger.info('L2 regularization on `{}`'.format(name)) loss_weight_decay += 0.5 * weight_decay * (param**2).sum() losses['loss_weight_decay'] = loss_weight_decay # total loss with GroupNode('loss').context_reg(): loss = sum(losses.values()) losses['loss'] = loss # for multi-GPU task, tell the GPUs to summarize the final loss O.utils.hint_loss_subgraph([loss_xent, loss_weight_decay], loss) # --------3.23----------- net = RawNetworkBuilder(inputs=[data, label], outputs=[f1, f2, f3, f4, f5, f6, f7, f8], loss=loss) # net = RawNetworkBuilder(inputs=[data, label], outputs=f1, loss=loss) metrics1 = get_metrics(f1, label_1) # metrics2 = get_metrics(f2, label_2) # metrics3 = get_metrics(f3, label_3) # metrics4 = get_metrics(f4, label_4) # metrics5 = get_metrics(f5, label_5) net.extra['extra_outputs'] = { 'pred_0': f1, 'pred_1': f1, 'pred_2': f2, 'pred_3': f3, 'pred_4': f4, 'pred_5': f5, 'pred_6': f6, 'pred_7': f7, 'label': label } # net.extra['extra_outputs'] = {'pred': f1, 'label': label} net.extra['extra_outputs'].update(metrics1) # net.extra['extra_outputs'].update(metrics2) # net.extra['extra_outputs'].update(metrics3) # net.extra['extra_outputs'].update(metrics4) # net.extra['extra_outputs'].update(metrics5) net.extra['extra_outputs'].update(losses) net.extra['extra_config'] = { 'monitor_vars': list(losses.keys()) + list(metrics1.keys()) } return net