From edac6a8dac9c4222ff9a719a4b2ec2fc06031655 Mon Sep 17 00:00:00 2001
From: Tianqi Chen <tqchen@users.noreply.github.com>
Date: Sat, 14 Apr 2018 19:25:26 -0700
Subject: [PATCH] Refactor, refactor code structure, fix pynq rpc (#29)
---
.../resnet18/pynq/imagenet_predict.py | 16 +-
vta/python/vta/__init__.py | 5 +-
vta/python/vta/exec/rpc_server.py | 10 +-
vta/python/vta/top/__init__.py | 5 +
vta/python/vta/{ => top}/arm_conv2d.py | 10 +-
vta/python/vta/{ => top}/vta_conv2d.py | 7 +-
.../integration/test_benchmark_topi_conv2d.py | 155 ++++++++++++++++++
vta/tests/python/pynq/test_benchmark_topi.py | 146 -----------------
8 files changed, 185 insertions(+), 169 deletions(-)
create mode 100644 vta/python/vta/top/__init__.py
rename vta/python/vta/{ => top}/arm_conv2d.py (97%)
rename vta/python/vta/{ => top}/vta_conv2d.py (98%)
create mode 100644 vta/tests/python/integration/test_benchmark_topi_conv2d.py
delete mode 100644 vta/tests/python/pynq/test_benchmark_topi.py
diff --git a/vta/examples/resnet18/pynq/imagenet_predict.py b/vta/examples/resnet18/pynq/imagenet_predict.py
index eb660ea12..ae8d49017 100644
--- a/vta/examples/resnet18/pynq/imagenet_predict.py
+++ b/vta/examples/resnet18/pynq/imagenet_predict.py
@@ -37,10 +37,10 @@ remote = rpc.connect(host, port)
vta.program_fpga(remote, BITSTREAM_FILE)
if verbose:
- logging.basicConfig(level=logging.INFO)
+ logging.basicConfig(level=logging.DEBUG)
-# Change to -device=vta-cpu to run cpu only inference.
-target = "llvm -device=vta"
+# Change to -device=vtacpu to run cpu only inference.
+target = tvm.target.create("llvm -device=vta")
target_host = "llvm -mtriple=armv7-none-linux-gnueabihf -mcpu=cortex-a9 -mattr=+neon"
synset = eval(open(os.path.join(CATEG_FILE)).read())
@@ -109,7 +109,7 @@ dtype = "float32"
sym = vta.graph.remove_stochastic(sym)
sym = vta.graph.clean_cast(sym)
sym = vta.graph.clean_conv_fuse(sym)
-if "vta" in target:
+if target.device_name == "vta":
sym = vta.graph.pack(sym, shape_dict, factor)
graph_attr.set_shape_inputs(sym, shape_dict)
@@ -118,10 +118,10 @@ graph_attr.set_dtype_inputs(sym, dtype_dict)
sym = sym.apply("InferType")
with nnvm.compiler.build_config(opt_level=3):
- if "vta" not in target:
+ if target.device_name != "vta":
graph, lib, params = nnvm.compiler.build(
- sym, target, shape_dict, dtype_dict,
- params=params, target_host=target_host)
+ sym, target_host, shape_dict, dtype_dict,
+ params=params)
else:
with vta.build_config():
graph, lib, params = nnvm.compiler.build(
@@ -133,7 +133,7 @@ temp = util.tempdir()
lib.save(temp.relpath("graphlib.o"))
remote.upload(temp.relpath("graphlib.o"))
lib = remote.load_module("graphlib.o")
-ctx = remote.ext_dev(0) if "vta" in target else remote.cpu(0)
+ctx = remote.ext_dev(0) if target.device_name == "vta" else remote.cpu(0)
print("Build complete...")
diff --git a/vta/python/vta/__init__.py b/vta/python/vta/__init__.py
index d0a3279f1..4be16ccfb 100644
--- a/vta/python/vta/__init__.py
+++ b/vta/python/vta/__init__.py
@@ -3,11 +3,12 @@ from __future__ import absolute_import as _abs
from .environment import get_env, Environment
-from . import arm_conv2d, vta_conv2d
-from .build_module import build_config, lower, build
from .rpc_client import reconfig_runtime, program_fpga
+
try:
+ from . import top
+ from .build_module import build_config, lower, build
from . import graph
except (ImportError, RuntimeError):
pass
diff --git a/vta/python/vta/exec/rpc_server.py b/vta/python/vta/exec/rpc_server.py
index 014b40564..f3db38c92 100644
--- a/vta/python/vta/exec/rpc_server.py
+++ b/vta/python/vta/exec/rpc_server.py
@@ -75,20 +75,20 @@ def server_start():
pkg = PkgConfig(cfg, proj_root)
# check if the configuration is already the same
if os.path.isfile(cfg_path):
- old_cfg = json.load(open(cfg_path))
+ old_cfg = json.loads(open(cfg_path, "r").read())
if pkg.same_config(old_cfg):
- logging.info("Skip reconfiguration because runtime config is the same")
+ logging.info("Skip reconfig_runtime due to same config.")
return
- cflags += ["-O2", "-std=c++11"]
+ cflags = ["-O2", "-std=c++11"]
cflags += pkg.cflags
ldflags = pkg.ldflags
lib_name = dll_path
- source = env.pkg_config.lib_source
+ source = pkg.lib_source
logging.info("Rebuild runtime: output=%s, cflags=%s, source=%s, ldflags=%s",
dll_path, str(cflags), str(source), str(ldflags))
cc.create_shared(lib_name, source, cflags + ldflags)
with open(cfg_path, "w") as outputfile:
- json.dump(pkg.cfg_json, outputfile)
+ outputfile.write(pkg.cfg_json)
def main():
diff --git a/vta/python/vta/top/__init__.py b/vta/python/vta/top/__init__.py
new file mode 100644
index 000000000..614ed2347
--- /dev/null
+++ b/vta/python/vta/top/__init__.py
@@ -0,0 +1,5 @@
+"""TVM TOPI connector, eventually most of these should go to TVM repo"""
+
+from .vta_conv2d import packed_conv2d, schedule_packed_conv2d
+from . import vta_conv2d
+from . import arm_conv2d
diff --git a/vta/python/vta/arm_conv2d.py b/vta/python/vta/top/arm_conv2d.py
similarity index 97%
rename from vta/python/vta/arm_conv2d.py
rename to vta/python/vta/top/arm_conv2d.py
index 9e46ee7f8..c959f1ee9 100644
--- a/vta/python/vta/arm_conv2d.py
+++ b/vta/python/vta/top/arm_conv2d.py
@@ -44,7 +44,7 @@ _SCHEDULES = [
Im2ColPack(7, 4, 1, 16, False),
]
-@_get_schedule.register(["tcpu", "vta"])
+@_get_schedule.register(["vtacpu", "vta"])
def _schedule_conv2d(wkl):
if wkl not in _WORKLOADS:
raise ValueError("no schedule for such workload: {}".format(wkl))
@@ -53,10 +53,10 @@ def _schedule_conv2d(wkl):
return sch
-@conv2d.register(["tcpu", "vta"])
+@conv2d.register(["vtacpu", "vta"])
def _declaration_conv2d(data, kernel, stride, padding, layout, out_dtype):
- assert layout == 'NCHW', "only support NCHW convolution on tcpu"
- assert data.shape[0].value == 1, "only support batch size=1 convolution on tcpu"
+ assert layout == 'NCHW', "only support NCHW convolution on vtacpu"
+ assert data.shape[0].value == 1, "only support batch size=1 convolution on vtacpu"
wkl = _get_workload(data, kernel, stride, padding, out_dtype)
sch = _get_schedule(wkl)
return _SCH_TO_DECL_FUNC[type(sch)](data, kernel, stride, padding, out_dtype)
@@ -284,7 +284,7 @@ def _schedule_im2col_conv2d(s, data, data_pad, data_col, data_vec,
return s
-@generic.schedule_conv2d_nchw.register(["tcpu", "vta"])
+@generic.schedule_conv2d_nchw.register(["vtacpu", "vta"])
def schedule_conv2d(outs):
"""Create schedule for tensors"""
s = tvm.create_schedule([x.op for x in outs])
diff --git a/vta/python/vta/vta_conv2d.py b/vta/python/vta/top/vta_conv2d.py
similarity index 98%
rename from vta/python/vta/vta_conv2d.py
rename to vta/python/vta/top/vta_conv2d.py
index 0baca7ba5..577eac8e1 100644
--- a/vta/python/vta/vta_conv2d.py
+++ b/vta/python/vta/top/vta_conv2d.py
@@ -1,4 +1,5 @@
"""Namespace for supporting packed_conv2d + ewise variant of nnvm."""
+from __future__ import absolute_import as _abs
from collections import namedtuple
@@ -7,7 +8,7 @@ import tvm
import topi
from nnvm.top import registry as reg, OpPattern
-from . import environment as vta
+from ..environment import get_env
Workload = namedtuple("Conv2DWorkload",
@@ -219,7 +220,7 @@ def schedule_packed_conv2d(outs):
wrkld = _get_workload(data, pad_data, kernel, output)
plan = _WL2PLAN[wrkld]
- env = vta.get_env()
+ env = get_env()
load_inp = load_wgt = load_out = store_out = env.dma_copy
alu = env.alu
@@ -251,7 +252,7 @@ def schedule_packed_conv2d(outs):
# tile
oc_factor = (plan.oc_factor if plan.oc_factor
- else wrkld.out_filter // vta.BLOCK_OUT)
+ else plan.out_filter // env.BLOCK_OUT)
h_factor = (plan.h_factor if plan.h_factor else oshape[2])
w_factor = (plan.w_factor if plan.w_factor else oshape[3])
diff --git a/vta/tests/python/integration/test_benchmark_topi_conv2d.py b/vta/tests/python/integration/test_benchmark_topi_conv2d.py
new file mode 100644
index 000000000..0a5edfdc7
--- /dev/null
+++ b/vta/tests/python/integration/test_benchmark_topi_conv2d.py
@@ -0,0 +1,155 @@
+"""Testing if we can generate code in topi style"""
+
+import tvm
+from tvm.contrib import util
+from tvm.contrib.pickle_memoize import memoize
+import topi
+import topi.testing
+import vta
+import vta.testing
+import numpy as np
+
+Workload = vta.top.vta_conv2d.Workload
+
+@tvm.tag_scope(tag=topi.tag.ELEMWISE)
+def my_clip(x, a_min, a_max):
+ """Unlike topi's current clip, put min and max into two stages."""
+ const_min = tvm.const(a_min, x.dtype)
+ const_max = tvm.const(a_max, x.dtype)
+ x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA")
+ x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
+ return x
+
+
+def test_vta_conv2d():
+ def run_vta_conv2d(env, remote, key, batch_size, wl, profile=True):
+ data_shape = (batch_size, wl.in_filter // env.BLOCK_IN,
+ wl.height, wl.width, env.BLOCK_IN)
+ kernel_shape = (wl.out_filter // env.BLOCK_OUT,
+ wl.in_filter // env.BLOCK_IN,
+ wl.hkernel, wl.wkernel,
+ env.BLOCK_OUT, env.BLOCK_IN)
+ bias_shape = (wl.out_filter // env.BLOCK_OUT, 1, 1, env.BLOCK_OUT)
+
+
+ fout_height = (wl.height + 2 * wl.hpad - wl.hkernel) // wl.hstride + 1
+ fout_width = (wl.width + 2 * wl.wpad - wl.wkernel) // wl.wstride + 1
+ data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype)
+ kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
+ bias = tvm.placeholder(bias_shape, name="kernel", dtype=env.acc_dtype)
+
+ res_conv = vta.top.packed_conv2d(
+ data, kernel, padding=(wl.hpad, wl.wpad), strides=(wl.hstride, wl.wstride))
+ res = topi.right_shift(res_conv, 8)
+ res = topi.broadcast_add(res, bias)
+ res = my_clip(res, 0, 127)
+ res = topi.cast(res, "int8")
+
+ num_ops = fout_height * fout_width * wl.hkernel * wl.wkernel * wl.out_filter * wl.in_filter
+
+ a_shape = (batch_size, wl.in_filter, wl.height, wl.width)
+ w_shape = (wl.out_filter, wl.in_filter, wl.hkernel, wl.wkernel)
+ stride = (wl.hstride, wl.wstride)
+ data_dtype = data.dtype
+ acc_dtype = env.acc_dtype
+ assert wl.hpad == wl.wpad
+ padding = wl.hpad
+
+ @memoize("vta.tests.test_benchmark_topi.conv2d,verify_nhwc")
+ def get_ref_data():
+ a_np = (np.random.uniform(size=a_shape) * 4).astype(data_dtype)
+ w_np = (np.random.uniform(size=w_shape) * 4).astype(data_dtype)
+ a_np = np.abs(a_np)
+ w_np = np.abs(w_np)
+ b_np = topi.testing.conv2d_nchw_python(
+ a_np.astype(acc_dtype), w_np.astype(acc_dtype), stride, padding).astype(acc_dtype)
+ return a_np, w_np, b_np
+
+
+ def verify(s, check_correctness):
+ mod = vta.build(s, [data, kernel, bias, res], "ext_dev",
+ env.target_host, name="conv2d")
+ temp = util.tempdir()
+
+ mod.save(temp.relpath("conv2d.o"))
+ remote.upload(temp.relpath("conv2d.o"))
+ f = remote.load_module("conv2d.o")
+ # verify
+ ctx = remote.ext_dev(0)
+ # Data in original format
+ data_orig, kernel_orig, res_ref = get_ref_data()
+ bias_orig = (np.random.uniform(size=(wl.out_filter,)) * 4).astype("int32")
+ bias_orig = np.abs(bias_orig)
+
+ data_packed = data_orig.reshape(
+ batch_size, wl.in_filter // env.BLOCK_IN, env.BLOCK_IN,
+ wl.height, wl.width).transpose((0, 1, 3, 4, 2))
+ kernel_packed = kernel_orig.reshape(
+ wl.out_filter // env.BLOCK_OUT, env.BLOCK_OUT,
+ wl.in_filter // env.BLOCK_IN, env.BLOCK_IN,
+ wl.hkernel, wl.wkernel).transpose((0, 2, 4, 5, 1, 3))
+ bias_packed = bias_orig.reshape(
+ wl.out_filter // env.BLOCK_OUT, 1, 1, env.BLOCK_OUT)
+ res_shape = topi.util.get_const_tuple(res.shape)
+
+ res_np = np.zeros(res_shape).astype(res.dtype)
+ data_arr = tvm.nd.array(data_packed, ctx)
+ kernel_arr = tvm.nd.array(kernel_packed, ctx)
+ bias_arr = tvm.nd.array(bias_packed, ctx)
+ res_arr = tvm.nd.array(res_np, ctx)
+ time_f = f.time_evaluator("conv2d", ctx, number=5)
+ cost = time_f(data_arr, kernel_arr, bias_arr, res_arr)
+ res_unpack = res_arr.asnumpy().transpose(
+ (0, 1, 4, 2, 3)).reshape(batch_size, wl.out_filter, fout_height, fout_width)
+ if check_correctness:
+ assert wl.hpad == wl.wpad
+ stride = (wl.hstride, wl.wstride)
+ padding = wl.hpad
+ res_ref = res_ref >> 8
+ res_ref += bias_orig.reshape(wl.out_filter, 1, 1)
+ res_ref = np.clip(res_ref, 0, 127).astype("int8")
+ np.testing.assert_allclose(res_unpack, res_ref)
+ return cost
+
+ def conv_normal(print_ir):
+ print("----- CONV2D End-to-End Test-------")
+ with vta.build_config():
+ s = vta.top.schedule_packed_conv2d([res])
+ if print_ir:
+ print(vta.lower(s, [data, kernel, bias, res], simple_mode=True))
+ cost = verify(s, True)
+ gops = (num_ops / cost.mean) / float(10 ** 9)
+ print("\tTime cost = %g sec/op, %g GFLOPS" % (cost.mean, gops))
+
+ conv_normal(False)
+
+ def _run(env, remote):
+ # ResNet18 workloads
+ resnet = {
+ # Workloads of resnet18 on imagenet
+ 0: Workload(224, 224, 16, 64, 7, 7, 3, 3, 2, 2),
+ 1: Workload(56, 56, 64, 64, 3, 3, 1, 1, 1, 1),
+ 2: Workload(56, 56, 64, 64, 1, 1, 0, 0, 1, 1),
+ 3: Workload(56, 56, 64, 128, 3, 3, 1, 1, 2, 2),
+ 4: Workload(56, 56, 64, 128, 1, 1, 0, 0, 2, 2),
+ 5: Workload(28, 28, 128, 128, 3, 3, 1, 1, 1, 1),
+ 6: Workload(28, 28, 128, 256, 3, 3, 1, 1, 2, 2),
+ 7: Workload(28, 28, 128, 256, 1, 1, 0, 0, 2, 2),
+ 8: Workload(14, 14, 256, 256, 3, 3, 1, 1, 1, 1),
+ 9: Workload(14, 14, 256, 512, 3, 3, 1, 1, 2, 2),
+ 10: Workload(14, 14, 256, 512, 1, 1, 0, 0, 2, 2),
+ 11: Workload(7, 7, 512, 512, 3, 3, 1, 1, 1, 1),
+ }
+
+ batch_size = 1
+ for i in range(0, len(resnet)):
+ wl = resnet[i]
+ key = "resnet-cfg[%d]" % i
+ print("key=%s" % key)
+ print(wl)
+ run_vta_conv2d(env, remote, key, batch_size, wl)
+ vta.testing.run(_run)
+
+
+if __name__ == "__main__":
+ test_vta_conv2d()
diff --git a/vta/tests/python/pynq/test_benchmark_topi.py b/vta/tests/python/pynq/test_benchmark_topi.py
deleted file mode 100644
index 3e2d19a67..000000000
--- a/vta/tests/python/pynq/test_benchmark_topi.py
+++ /dev/null
@@ -1,146 +0,0 @@
-"""Testing if we can generate code in topi style"""
-
-import topi
-import tvm
-from tvm.contrib import util, rpc
-import vta
-from vta import vta_conv2d
-import numpy as np
-import mxnet as mx
-
-Workload = vta_conv2d.Workload
-
-@tvm.tag_scope(tag=topi.tag.ELEMWISE)
-def my_clip(x, a_min, a_max):
- """Unlike topi's current clip, put min and max into two stages."""
- const_min = tvm.const(a_min, x.dtype)
- const_max = tvm.const(a_max, x.dtype)
- x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA")
- x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
- return x
-
-host = "pynq"
-port = 9091
-target = "llvm -target=armv7-none-linux-gnueabihf -mattr=+neon"
-print_ir = False
-
-
-def test_vta_conv2d(key, batch_size, wl, profile=True):
- env = vta.get_env()
- data_shape = (batch_size, wl.in_filter // env.BLOCK_IN,
- wl.height, wl.width, env.BLOCK_IN)
- kernel_shape = (wl.out_filter // env.BLOCK_OUT,
- wl.in_filter // env.BLOCK_IN,
- wl.hkernel, wl.wkernel,
- env.BLOCK_OUT, env.BLOCK_IN)
- bias_shape = (wl.out_filter // env.BLOCK_OUT, 1, 1, env.BLOCK_OUT)
-
-
- fout_height = (wl.height + 2 * wl.hpad - wl.hkernel) // wl.hstride + 1
- fout_width = (wl.width + 2 * wl.wpad - wl.wkernel) // wl.wstride + 1
- data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype)
- kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
- bias = tvm.placeholder(bias_shape, name="kernel", dtype=env.acc_dtype)
-
- res_conv = vta_conv2d.packed_conv2d(
- data, kernel, padding=(wl.hpad, wl.wpad), strides=(wl.hstride, wl.wstride))
- res = topi.right_shift(res_conv, 8)
- res = topi.broadcast_add(res, bias)
- res = my_clip(res, 0, 127)
- res = topi.cast(res, "int8")
-
- num_ops = fout_height * fout_width * wl.hkernel * wl.wkernel * wl.out_filter * wl.in_filter
-
- def verify(s, check_correctness):
- mod = tvm.build(s, [data, kernel, bias, res], "ext_dev", target, name="conv2d")
- temp = util.tempdir()
- remote = rpc.connect(host, port)
-
- mod.save(temp.relpath("conv2d.o"))
- remote.upload(temp.relpath("conv2d.o"))
- f = remote.load_module("conv2d.o")
- # verify
- ctx = remote.ext_dev(0)
- # Data in original format
- data_orig = (np.random.uniform(
- size=(batch_size, wl.in_filter, wl.height, wl.width)) * 4).astype(data.dtype)
- kernel_orig = (np.random.uniform(
- size=(wl.out_filter, wl.in_filter, wl.hkernel, wl.wkernel)) * 4).astype(kernel.dtype)
- bias_orig = (np.random.uniform(size=(wl.out_filter,)) * 4).astype("int32")
-
- data_orig = np.abs(data_orig)
- kernel_orig = np.abs(kernel_orig)
- bias_orig = np.abs(bias_orig)
-
- data_packed = data_orig.reshape(
- batch_size, wl.in_filter // env.BLOCK_IN, env.BLOCK_IN,
- wl.height, wl.width).transpose((0, 1, 3, 4, 2))
- kernel_packed = kernel_orig.reshape(
- wl.out_filter // env.BLOCK_OUT, env.BLOCK_OUT,
- wl.in_filter // env.BLOCK_IN, env.BLOCK_IN,
- wl.hkernel, wl.wkernel).transpose((0, 2, 4, 5, 1, 3))
- bias_packed = bias_orig.reshape(
- wl.out_filter // env.BLOCK_OUT, 1, 1, env.BLOCK_OUT)
- res_shape = topi.util.get_const_tuple(res.shape)
-
- res_np = np.zeros(res_shape).astype(res.dtype)
- data_arr = tvm.nd.array(data_packed, ctx)
- kernel_arr = tvm.nd.array(kernel_packed, ctx)
- bias_arr = tvm.nd.array(bias_packed, ctx)
- res_arr = tvm.nd.array(res_np, ctx)
- time_f = f.time_evaluator("conv2d", ctx, number=10)
- cost = time_f(data_arr, kernel_arr, bias_arr, res_arr)
- res_unpack = res_arr.asnumpy().transpose(
- (0, 1, 4, 2, 3)).reshape(batch_size, wl.out_filter, fout_height, fout_width)
- if check_correctness:
- res_ref = mx.nd.Convolution(
- mx.nd.array(data_orig.astype(env.acc_dtype), mx.cpu(0)),
- mx.nd.array(kernel_orig.astype(env.acc_dtype), mx.cpu(0)),
- stride=(wl.hstride, wl.wstride),
- kernel=(wl.hkernel, wl.wkernel),
- num_filter=wl.out_filter,
- no_bias=True,
- pad=(wl.hpad, wl.wpad)).asnumpy().astype(env.acc_dtype)
- res_ref = res_ref >> 8
- res_ref += bias_orig.reshape(wl.out_filter, 1, 1)
- res_ref = np.clip(res_ref, 0, 127).astype("int8")
- np.testing.assert_allclose(res_unpack, res_ref)
- print("Correctness check pass...")
- return cost
-
- def conv_normal(print_ir):
- print("----- CONV2D End-to-End Test-------")
- with vta.build_config():
- s = vta_conv2d.schedule_packed_conv2d([res])
- if print_ir:
- print(vta.lower(s, [data, kernel, bias, res], simple_mode=True))
- cost = verify(s, True)
- gops = (num_ops / cost.mean) / float(10 ** 9)
- print("\tTime cost = %g sec/op, %g GFLOPS" % (cost.mean, gops))
-
- conv_normal(print_ir)
-
-# ResNet18 workloads
-resnet = {
- # Workloads of resnet18 on imagenet
- 0: Workload(224, 224, 16, 64, 7, 7, 3, 3, 2, 2),
- 1: Workload(56, 56, 64, 64, 3, 3, 1, 1, 1, 1),
- 2: Workload(56, 56, 64, 64, 1, 1, 0, 0, 1, 1),
- 3: Workload(56, 56, 64, 128, 3, 3, 1, 1, 2, 2),
- 4: Workload(56, 56, 64, 128, 1, 1, 0, 0, 2, 2),
- 5: Workload(28, 28, 128, 128, 3, 3, 1, 1, 1, 1),
- 6: Workload(28, 28, 128, 256, 3, 3, 1, 1, 2, 2),
- 7: Workload(28, 28, 128, 256, 1, 1, 0, 0, 2, 2),
- 8: Workload(14, 14, 256, 256, 3, 3, 1, 1, 1, 1),
- 9: Workload(14, 14, 256, 512, 3, 3, 1, 1, 2, 2),
- 10: Workload(14, 14, 256, 512, 1, 1, 0, 0, 2, 2),
- 11: Workload(7, 7, 512, 512, 3, 3, 1, 1, 1, 1),
-}
-
-batch_size = 1
-for i in range(0, len(resnet)):
- wl = resnet[i]
- key = "resnet-cfg[%d]" % i
- print "key=%s" % key
- print wl
- test_vta_conv2d(key, batch_size, wl)
--
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