[TOP][Example] register pool, global_pool; add mobilenet example (#32)

* register pool, global_pool; add mobilenet example

* tests of pool and global_pool

* use new API of runtime module

* small fix

nnvm/example/mobilenet_inference_gpu.py

+"""Forward propagation of MobileNet on GPU."""
+import numpy as np
+import time
+import os
+
+import tvm
+import topi
+import nnvm.symbol as sym
+import nnvm.compiler
+import nnvm.runtime
+from tvm.contrib import nvcc
+
+TASK="mobilenet"
+
+target = 'cuda'
+ctx = tvm.gpu(0)
+
+@tvm.register_func
+def tvm_callback_cuda_compile(code):
+    ptx = nvcc.compile_cuda(code, target="ptx", options=["-arch=sm_60"])
+    return ptx
+
+def write_code(code, fname):
+    with open(fname, "w") as f:
+        f.write(code)
+
+@tvm.register_func
+def tvm_callback_cuda_postproc(code):
+    if not os.path.exists("perf"):
+        os.mkdir("perf")
+    write_code(code, "perf/%s_generated.cu" % TASK)
+    return code
+
+dtype = 'float32'
+epsilon = 1e-10 + 1e-5
+
+def conv_block(data, name, channels, kernel_size=(3,3), strides=(1,1), padding=(1,1)):
+    # convolution + bn + relu
+    conv = sym.conv2d(data=data, channels=channels, kernel_size=kernel_size, strides=strides,
+        padding=padding, use_bias=False, layout='NCHW', name=name + '_conv')
+    bn = sym.batch_norm(data=conv, epsilon=epsilon, name=name + '_bn')
+    act = sym.relu(data=bn, name=name + '_relu')
+    return act
+
+def separable_conv_block(data, name, depthwise_channels, pointwise_channels, kernel_size=(3,3), downsample=False, padding=(1,1)):
+    if downsample:
+        strides = (2,2)
+    else:
+        strides = (1,1)
+    # depthwise convolution + bn + relu
+    conv1 = sym.conv2d(data=data, channels=depthwise_channels, groups=depthwise_channels, kernel_size=kernel_size, strides=strides,
+        padding=padding, use_bias=False, layout='NCHW', name=name + '_conv1')
+    bn1 = sym.batch_norm(data=conv1, epsilon=epsilon, name=name + '_bn1')
+    act1 = sym.relu(data=bn1, name=name + '_relu1')
+    # pointwise convolution + bn + relu
+    conv2 = sym.conv2d(data=act1, channels=pointwise_channels, kernel_size=(1,1), strides=(1,1),
+        padding=(0,0), use_bias=False, layout='NCHW', name=name + '_conv2')
+    bn2 = sym.batch_norm(data=conv2, epsilon=epsilon, name=name + '_bn2')
+    act2 = sym.relu(data=bn2, name=name + '_relu2')
+    return act2
+
+def mobile_net(num_classes=1000, alpha=1.0, is_shallow=False):
+    data = sym.Variable("data")
+    body = conv_block(data, 'conv_block_1', int(32*alpha), strides=(2,2))
+    body = separable_conv_block(body, 'separable_conv_block_1', int(32*alpha), int(64*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_2', int(64*alpha), int(128*alpha), downsample=True)
+    body = separable_conv_block(body, 'separable_conv_block_3', int(128*alpha), int(128*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_4', int(128*alpha), int(256*alpha), downsample=True)
+    body = separable_conv_block(body, 'separable_conv_block_5', int(256*alpha), int(256*alpha))
+    body = separable_conv_block(body, 'separable_conv_block_6', int(256*alpha), int(512*alpha), downsample=True)
+    if is_shallow:
+        body = separable_conv_block(body, 'separable_conv_block_7', int(512*alpha), int(1024*alpha), downsample=True)
+        body = separable_conv_block(body, 'separable_conv_block_8', int(1024*alpha), int(1024*alpha))
+    else:
+        for i in range(7, 12):
+            body = separable_conv_block(body, 'separable_conv_block_%d' % i, int(512*alpha), int(512*alpha))
+        body = separable_conv_block(body, 'separable_conv_block_12', int(512*alpha), int(1024*alpha), downsample=True)
+        body = separable_conv_block(body, 'separable_conv_block_13', int(1024*alpha), int(1024*alpha))
+    pool = sym.global_avg_pool2d(data=body, name='pool')
+    flatten = sym.flatten(data=pool, name='flatten')
+    fc = sym.dense(data=flatten, units=num_classes, use_bias=False, name='fc')
+    softmax = sym.softmax(data=fc, name='softmax')
+    return softmax
+
+
+batch_size = 1
+num_classes = 1000
+image_shape = (3,224,224)
+data_shape = (batch_size,) + image_shape
+out_shape = (batch_size, num_classes)
+
+net = mobile_net(num_classes=num_classes, alpha=1.0, is_shallow=False)
+
+# build graph
+with nnvm.compiler.build_config(opt_level=2):
+    graph, lib, _ = nnvm.compiler.build(net, target, {'data': data_shape})
+# prepare params
+params = {}
+names = graph.index.input_names
+shapes = [graph.json_attr("shape")[graph.index.entry_id(x)] for x in names]
+for i in range(len(names)):
+    params[names[i]] = tvm.nd.array(np.random.uniform(-0.1, 0.1, size=shapes[i]).astype(dtype), ctx=ctx)
+# create runtime module
+module = nnvm.runtime.create(graph, lib, ctx)
+# set input
+module.set_input(**params)
+# run
+print("run")
+module.run()
+ctx.sync()
+start = time.time()
+for i in range(1000):
+    module.run()
+    ctx.sync()
+print("average time cost of 1000 runs = %g ms" % ((time.time() - start)))
+# get output
+out = module.get_output(0, tvm.nd.empty(out_shape, dtype))

nnvm/include/nnvm/top/nn.h

@@ -202,7 +202,6 @@ struct Pool2DParam : public dmlc::Parameter<Pool2DParam> {
   TShape pool_size;
   TShape strides;
   TShape padding;
-  int groups;
   int layout;
   bool ceil_mode;
 
@@ -214,12 +213,6 @@ struct Pool2DParam : public dmlc::Parameter<Pool2DParam> {
     DMLC_DECLARE_FIELD(padding).set_default(TShape({0, 0}))
       .describe("If padding is non-zero, then the input is implicitly zero-padded"
                 "on both sides for padding number of points");
-    DMLC_DECLARE_FIELD(groups).set_default(1)
-      .describe("Controls the connections between inputs and outputs."
-                "At groups=1, all inputs are convolved to all outputs."
-                "At groups=2, the operation becomes equivalent to having two convolution"
-                "layers side by side, each seeing half the input channels, and producing"
-                "half the output channels, and both subsequently concatenated.");
     DMLC_DECLARE_FIELD(layout)
       .add_enum("NCHW", kNCHW)
       .add_enum("NHWC", kNHWC)

nnvm/python/nnvm/top/nn.py

@@ -18,6 +18,7 @@ def compute_relu(attrs, inputs, _):
 reg.register_schedule("relu", _fschedule_broadcast)
 reg.register_pattern("relu", OpPattern.ELEMWISE)
 
+
 # leaky_relu
 @reg.register_compute("leaky_relu")
 def compute_leaky_relu(attrs, inputs, _):
@@ -27,6 +28,7 @@ def compute_leaky_relu(attrs, inputs, _):
 reg.register_schedule("leaky_relu", _fschedule_broadcast)
 reg.register_pattern("leaky_relu", OpPattern.ELEMWISE)
 
+
 # flatten
 @reg.register_compute("flatten")
 def compute_flatten(attrs, inputs, _):
@@ -73,11 +75,10 @@ def schedule_dense(_, outs, target):
     # naive schedule
     return tvm.create_schedule([x.op for x in outs])
 
-# register extern for now, change me when fusion is enabled.
 reg.register_pattern("dense", OpPattern.OUT_ELEMWISE_FUSABLE)
 
 
-# conv
+# conv2d
 @reg.register_compute("conv2d")
 def compute_conv2d(attrs, inputs, _):
     """Compute definition of conv2d"""
@@ -113,3 +114,89 @@ def schedule_conv2d(attrs, outs, target):
     return tvm.create_schedule([x.op for x in outs])
 
 reg.register_pattern("conv2d", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
+# max_pool2d
+@reg.register_compute("max_pool2d")
+def compute_max_pool2d(attrs, inputs, _):
+    """Compute definition of max_pool2d"""
+    pool_size = attrs.get_int_tuple("pool_size")
+    strides = attrs.get_int_tuple("strides")
+    padding = attrs.get_int_tuple("padding")
+    layout = attrs["layout"]
+    ceil_mode = attrs["ceil_mode"]
+    assert layout == "NCHW", "only support nchw for now"
+    assert ceil_mode == "False", "not support ceil_mode now"
+    return topi.nn.pool(inputs[0], pool_size, strides, padding, pool_type='max')
+
+@reg.register_schedule("max_pool2d")
+def schedule_max_pool2d(_, outs, target):
+    """Schedule definition of max_pool2d"""
+    if target == "cuda":
+        return topi.cuda.schedule_pool(outs)
+    # naive schedule
+    return tvm.create_schedule([x.op for x in outs])
+
+reg.register_pattern("max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
+# avg_pool2d
+@reg.register_compute("avg_pool2d")
+def compute_avg_pool2d(attrs, inputs, _):
+    """Compute definition of avg_pool2d"""
+    pool_size = attrs.get_int_tuple("pool_size")
+    strides = attrs.get_int_tuple("strides")
+    padding = attrs.get_int_tuple("padding")
+    layout = attrs["layout"]
+    ceil_mode = attrs["ceil_mode"]
+    assert layout == "NCHW", "only support nchw for now"
+    assert ceil_mode == "False", "not support ceil_mode now"
+    return topi.nn.pool(inputs[0], pool_size, strides, padding, pool_type='avg')
+
+@reg.register_schedule("avg_pool2d")
+def schedule_avg_pool2d(_, outs, target):
+    """Schedule definition of avg_pool2d"""
+    if target == "cuda":
+        return topi.cuda.schedule_pool(outs)
+    # naive schedule
+    return tvm.create_schedule([x.op for x in outs])
+
+reg.register_pattern("avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
+# global_max_pool2d
+@reg.register_compute("global_max_pool2d")
+def compute_global_max_pool2d(attrs, inputs, _):
+    """Compute definition of global_max_pool2d"""
+    layout = attrs["layout"]
+    assert layout == "NCHW", "only support nchw for now"
+    return topi.nn.global_pool(inputs[0], pool_type='max')
+
+@reg.register_schedule("global_max_pool2d")
+def schedule_global_max_pool2d(_, outs, target):
+    """Schedule definition of global_max_pool2d"""
+    if target == "cuda":
+        return topi.cuda.schedule_global_pool(outs)
+    # naive schedule
+    return tvm.create_schedule([x.op for x in outs])
+
+reg.register_pattern("global_max_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
+# global_avg_pool2d
+@reg.register_compute("global_avg_pool2d")
+def compute_global_avg_pool2d(attrs, inputs, _):
+    """Compute definition of global_avg_pool2d"""
+    layout = attrs["layout"]
+    assert layout == "NCHW", "only support nchw for now"
+    return topi.nn.global_pool(inputs[0], pool_type='avg')
+
+@reg.register_schedule("global_avg_pool2d")
+def schedule_global_avg_pool2d(_, outs, target):
+    """Schedule definition of global_avg_pool2d"""
+    if target == "cuda":
+        return topi.cuda.schedule_global_pool(outs)
+    # naive schedule
+    return tvm.create_schedule([x.op for x in outs])
+
+reg.register_pattern("global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)

nnvm/tests/python/compiler/test_top_level1.py

@@ -16,7 +16,6 @@ def test_relu():
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
         data = np.random.uniform(size=dshape).astype(dtype)
         m.run(x=data)
         data = (data < 0) * data * 0.3 + (data>0) * data - 0.2
@@ -34,17 +33,10 @@ def test_exp():
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        set_input("x", data)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
-        y_np = np.exp(data.asnumpy())
+        data = np.random.uniform(size=dshape).astype(dtype)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        y_np = np.exp(data)
         np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
 
 
@@ -58,17 +50,10 @@ def test_log():
         with nnvm.compiler.build_config(opt_level=1):
             graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        set_input("x", data)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
-        y_np = np.log(data.asnumpy())
+        data = np.random.uniform(size=dshape).astype(dtype)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        y_np = np.log(data)
         np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
 
 
@@ -82,17 +67,10 @@ def test_tanh():
         with nnvm.compiler.build_config(opt_level=1):
             graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        set_input("x", data)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
-        y_np = np.sinh(data.asnumpy()) / np.cosh(data.asnumpy())
+        data = np.random.uniform(size=dshape).astype(dtype)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        y_np = np.sinh(data) / np.cosh(data)
         np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
 
 
@@ -105,17 +83,10 @@ def test_sigmoid():
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        set_input("x", data)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
-        y_np = 1.0 / (1.0 + np.exp(-data.asnumpy()))
+        data = np.random.uniform(size=dshape).astype(dtype)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        y_np = 1.0 / (1.0 + np.exp(-data))
         np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
 
 
@@ -129,17 +100,10 @@ def test_softmax():
         with nnvm.compiler.build_config(opt_level=1):
             graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
-        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
-        set_input("x", data)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
-        y_np = topi.testing.softmax_python(data.asnumpy())
+        data = np.random.uniform(size=dshape).astype(dtype)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        y_np = topi.testing.softmax_python(data)
         np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
 
 

nnvm/tests/python/compiler/test_top_level2.py

@@ -10,8 +10,8 @@ from nnvm.testing.config import ctx_list
 
 def test_conv2d():
     x = sym.Variable("x")
-    y = sym.conv2d(x, channels=10, kernel_size=(3, 3),
-                   name="y", use_bias=False, padding=(1,1))
+    y = sym.conv2d(x, channels=10, kernel_size=(3,3),
+                   name="y", padding=(1,1))
     dtype = "float32"
     dshape = (1, 3, 18, 18)
     kshape = (10, 3, 3, 3)
@@ -20,26 +20,20 @@ def test_conv2d():
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
         m = nnvm.runtime.create(graph, lib, ctx)
-        # get member functions
-        set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
-        # set input
         data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
         kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
-        set_input("x", data)
-        set_input("y_weight", kernel)
-        # execute
-        run()
-        # get output
-        out = tvm.nd.empty(oshape, dtype)
-        get_output(0, out)
+        bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
+        m.run(x=data, y_weight=kernel, y_bias=bias)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
         c_np = topi.testing.conv2d_nchw_python(
             data.asnumpy(), kernel.asnumpy(), 1, 1)
+        c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
         np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
 
 
 def test_grouped_conv2d():
     x = sym.Variable("x")
-    y = sym.conv2d(x, channels=32, kernel_size=(3, 3), groups=32,
+    y = sym.conv2d(x, channels=32, kernel_size=(3,3), groups=32,
                    name="y", padding=(1,1))
     dtype = "float32"
     dshape = (1, 32, 18, 18)
@@ -49,12 +43,10 @@ def test_grouped_conv2d():
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
         m = nnvm.runtime.create(graph, lib, ctx)
-        # set input
         data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
         kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
         bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
         m.run(x=data, y_weight=kernel, y_bias=bias)
-        # get output
         out = m.get_output(0, tvm.nd.empty(oshape, dtype))
         c_np = topi.testing.depthwise_conv2d_python_nchw(
             data.asnumpy(), kernel.asnumpy(), (1,1), 'SAME')
@@ -62,6 +54,78 @@ def test_grouped_conv2d():
         np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
 
 
+def test_max_pool2d():
+    x = sym.Variable("x")
+    y = sym.max_pool2d(x, pool_size=(2,2), strides=(2,2), padding=(0,0), name="y")
+    dtype = "float32"
+    dshape = (1, 3, 28, 28)
+    oshape = (1, 3, 14, 14)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = nnvm.runtime.create(graph, lib, ctx)
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = np.max(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5))
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
+
+
+def test_avg_pool2d():
+    x = sym.Variable("x")
+    y = sym.avg_pool2d(x, pool_size=(2,2), strides=(2,2), padding=(0,0), name="y")
+    dtype = "float32"
+    dshape = (1, 3, 28, 28)
+    oshape = (1, 3, 14, 14)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = nnvm.runtime.create(graph, lib, ctx)
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = np.mean(data.asnumpy().reshape(1,3,14,2,14,2), axis=(3,5))
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
+
+
+def test_global_max_pool2d():
+    x = sym.Variable("x")
+    y = sym.global_max_pool2d(x, name="y")
+    dtype = "float32"
+    dshape = (1, 1024, 7, 7)
+    oshape = (1, 1024, 1, 1)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = nnvm.runtime.create(graph, lib, ctx)
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = np.max(data.asnumpy(), axis=(2,3), keepdims=True)
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
+
+
+def test_global_avg_pool2d():
+    x = sym.Variable("x")
+    y = sym.global_avg_pool2d(x, name="y")
+    dtype = "float32"
+    dshape = (1, 1024, 7, 7)
+    oshape = (1, 1024, 1, 1)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = nnvm.runtime.create(graph, lib, ctx)
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = np.mean(data.asnumpy(), axis=(2,3), keepdims=True)
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
+
+
 if __name__ == "__main__":
     test_conv2d()
     test_grouped_conv2d()
+    test_max_pool2d()
+    test_avg_pool2d()
+    test_global_max_pool2d()
+    test_global_avg_pool2d()

nnvm/tests/python/frontend/mxnet/test_forward.py

@@ -23,7 +23,7 @@ def default_ctx():
     else:
         return tvm.cpu(0)
 
-def test_mxnet_frontend_impl(mx_symbol, data_shape=(2, 3, 224, 224), out_shape=(2, 1000)):
+def test_mxnet_frontend_impl(mx_symbol, data_shape=(1, 3, 224, 224), out_shape=(1, 1000)):
     def get_mxnet_output(symbol, x, dtype='float32'):
         from collections import namedtuple
         Batch = namedtuple('Batch', ['data'])
@@ -83,6 +83,5 @@ def test_forward_resnet():
 
 if __name__ == '__main__':
     test_forward_mlp()
-    # waiting for max_pool2d
-    # test_forward_vgg()
-    # test_forward_resnet()
+    test_forward_vgg()
+    test_forward_resnet()