Merge branch 'master' of https://www.github.com/jiazhihao/taso

examples/inceptionv3.py

@@ -0,0 +1,142 @@
+import taso
+
+def get_pads(kernel, padding):
+    if sum(padding) == 0 and sum(kernel) > 2:
+        pads = "VALID"
+    else:
+        pads = "SAME"
+    return pads
+
+def conv2d(graph, v, out_channels, kernel=(1, 1), stride=(1, 1), padding=(0, 0)):
+    w = graph.new_weight(dims=(out_channels, v.dim(1), *kernel))
+    v = graph.conv2d(input=v, weight=w, strides=stride, padding=get_pads(kernel, padding), activation="RELU")
+    return v
+
+def pool2d(graph, v, pool_type, kernel=(1, 1), stride=(1, 1), padding=(0, 0)):
+    if pool_type == 'global_avg':
+        pads = "VALID"
+        x = graph.avgpool2d(input=v, kernels=kernel, strides=[1, 1], padding=pads)
+    elif pool_type == 'avg':
+        pads = get_pads(kernel, padding)
+        x = graph.avgpool2d(input=v, kernels=kernel, strides=stride, padding=pads)
+    elif pool_type == 'max':
+        pads = get_pads(kernel, padding)
+        x = graph.maxpool2d(input=v, kernels=kernel, strides=stride, padding=pads)
+    else:
+        raise NotImplemented
+    return x
+
+
+def inception_front(graph, v):  # 3 x 299 x 299
+    v = conv2d(graph, v, out_channels=32, kernel=(3, 3), stride=(2, 2))  # 32 x 149 x 149
+    v = conv2d(graph, v, out_channels=32, kernel=(3, 3))  # 32 x 147 x 147
+    v = conv2d(graph, v, out_channels=64, kernel=(3, 3), padding=(1, 1))  # 64 x 147 x 147
+    v = pool2d(graph, v, pool_type='max', kernel=(3, 3), stride=(2, 2))  # 64 x 73 x 73
+    v = conv2d(graph, v, 80, kernel=(1, 1))  # 80 x 73 x 73
+    v = conv2d(graph, v, out_channels=192, kernel=(3, 3))  # 192 x 71 x 71
+    v = pool2d(graph, v, pool_type='max', kernel=(3, 3), stride=(2, 2))  # 192 x 35 x 35
+    return v
+
+
+def inception_a(graph, v, pool_features):
+    v1x1 = conv2d(graph, v, out_channels=64, kernel=(1, 1))
+
+    v5x5 = conv2d(graph, v, out_channels=48, kernel=(1, 1))
+    v5x5 = conv2d(graph, v5x5, out_channels=64, kernel=(5, 5), padding=(2, 2))
+
+    v3x3dbl = conv2d(graph, v, out_channels=64, kernel=(1, 1))
+    v3x3dbl = conv2d(graph, v3x3dbl, out_channels=96, kernel=(3, 3), padding=(1, 1))
+    v3x3dbl = conv2d(graph, v3x3dbl, out_channels=96, kernel=(3, 3), padding=(1, 1))
+
+    v_pool = pool2d(graph, v, pool_type='avg', kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+    v_pool = conv2d(graph, v_pool, out_channels=pool_features, kernel=(1, 1))
+    return graph.concat(1, [v1x1, v5x5, v3x3dbl, v_pool])
+
+
+def inception_b(graph, v):
+    v3x3 = conv2d(graph, v, out_channels=384, kernel=(3, 3), stride=(2, 2))
+
+    v3x3dbl = conv2d(graph, v, out_channels=64, kernel=(1, 1))
+    v3x3dbl = conv2d(graph, v3x3dbl, out_channels=96, kernel=(3, 3), padding=(1, 1))
+    v3x3dbl = conv2d(graph, v3x3dbl, out_channels=96, kernel=(3, 3), stride=(2, 2))
+
+    v_pool = pool2d(graph, v, pool_type='max', kernel=(3, 3), stride=(2, 2))
+    return graph.concat(1, [v3x3, v3x3dbl, v_pool]);
+
+
+def inception_c(graph, v, channels_7x7):
+    v1x1 = conv2d(graph, v, out_channels=192, kernel=(1, 1))
+
+    c7 = channels_7x7
+    v7x7 = conv2d(graph, v, out_channels=c7, kernel=(1, 1))
+    v7x7 = conv2d(graph, v7x7, out_channels=c7, kernel=(1, 7), padding=(0, 3))
+    v7x7 = conv2d(graph, v7x7, out_channels=192, kernel=(7, 1), padding=(3, 0))
+
+    v7x7dbl = conv2d(graph, v, out_channels=c7, kernel=(1, 1))
+    v7x7dbl = conv2d(graph, v7x7dbl, out_channels=c7, kernel=(7, 1), padding=(3, 0))
+    v7x7dbl = conv2d(graph, v7x7dbl, out_channels=c7, kernel=(1, 7), padding=(0, 3))
+    v7x7dbl = conv2d(graph, v7x7dbl, out_channels=c7, kernel=(7, 1), padding=(3, 0))
+    v7x7dbl = conv2d(graph, v7x7dbl, out_channels=192, kernel=(1, 7), padding=(0, 3))
+
+    v_pool = pool2d(graph, v, pool_type='avg', kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+    v_pool = conv2d(graph, v_pool, out_channels=192, kernel=(1, 1))
+    return graph.concat(1, [v1x1, v7x7, v7x7dbl, v_pool])
+
+
+def inception_d(graph, v):
+    v3x3 = conv2d(graph, v, out_channels=192, kernel=(1, 1))
+    v3x3 = conv2d(graph, v3x3, out_channels=320, kernel=(3, 3), stride=(2, 2))
+
+    v7x7x3 = conv2d(graph, v, out_channels=192, kernel=(1, 1))
+    v7x7x3 = conv2d(graph, v7x7x3, out_channels=192, kernel=(1, 7), padding=(0, 3))
+    v7x7x3 = conv2d(graph, v7x7x3, out_channels=192, kernel=(7, 1), padding=(3, 0))
+    v7x7x3 = conv2d(graph, v7x7x3, out_channels=192, kernel=(3, 3), stride=(2, 2))
+
+    v_pool = pool2d(graph, v, pool_type='max', kernel=(3, 3), stride=(2, 2))
+    return graph.concat(1, [v3x3, v7x7x3, v_pool])
+
+
+def inception_e(graph, v):
+    v1x1 = conv2d(graph, v, out_channels=320, kernel=(1, 1))
+
+    v3x3 = conv2d(graph, v, out_channels=384, kernel=(1, 1))
+    v3x3a = conv2d(graph, v3x3, out_channels=384, kernel=(1, 3), padding=(0, 1))
+    v3x3b = conv2d(graph, v3x3, out_channels=384, kernel=(3, 1), padding=(1, 0))
+
+    v3x3dbl = conv2d(graph, v, out_channels=448, kernel=(1, 1))
+    v3x3dbl = conv2d(graph, v3x3dbl, out_channels=384, kernel=(3, 3), padding=(1, 1))
+    v3x3dbla = conv2d(graph, v3x3dbl, out_channels=384, kernel=(1, 3), padding=(0, 1))
+    v3x3dblb = conv2d(graph, v3x3dbl, out_channels=384, kernel=(3, 1), padding=(1, 0))
+
+    v_pool = pool2d(graph, v, pool_type='avg', kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+    v_pool = conv2d(graph, v_pool, out_channels=192, kernel=(1, 1))
+    return graph.concat(1, [v1x1, v3x3a, v3x3b, v3x3dbla, v3x3dblb, v_pool])
+
+
+def inception_logits(graph, v):
+    return pool2d(graph, v, pool_type='global_avg')
+
+
+def inception_v3(batch_size=1):
+    graph = taso.new_graph()
+    v = graph.new_input(dims=(batch_size, 3, 299, 299))
+    v = inception_front(graph, v)
+    v = inception_a(graph, v, 32)
+    v = inception_a(graph, v, 64)
+    v = inception_a(graph, v, 64)
+    v = inception_b(graph, v)
+    v = inception_c(graph, v, 128)
+    v = inception_c(graph, v, 160)
+    v = inception_c(graph, v, 160)
+    v = inception_c(graph, v, 192)
+    v = inception_d(graph, v)
+    v = inception_e(graph, v)
+    v = inception_e(graph, v)
+    v = inception_logits(graph, v)
+    return graph
+
+graph = inception_v3(batch_size=32)  # change batch_size from 4 to 8 would cause error.
+opt_graph = taso.optimize(graph, alpha=1.0, budget=30)
+
+print(graph.run_time())
+print(opt_graph.run_time())