def test_simple_model_train(self):
with jt.flag_scope(trace_py_var=2):
model = Model(input_size=1)
opt = jt.optim.SGD(model.parameters(), 0.1)
batch_size = 10
x = jt.float32(np.random.rand(batch_size, 1))
y = model(x)
opt.step(y**2)
jt.sync_all()
data = jt.dump_trace_data()
jt.clear_trace_data()
# print_stack_tree(data)
for k, v in data["execute_op_info"].items():
for i in v['fused_ops']:
if i not in data["node_data"]:
assert 0, (i, "not found")
for k, v in list(data["node_data"].items()):
if v["attrs"]["name"] == "unname":
assert 0
print(len(data["node_data"]))
with open(f"{jt.flags.cache_path}/simple_model_train.pkl",
"wb") as f:
pickle.dump(data, f)
def test_resnet_infer_with_feature(self):
cat_url = "https://ss1.bdstatic.com/70cFuXSh_Q1YnxGkpoWK1HF6hhy/it/u=3782485413,1118109468&fm=26&gp=0.jpg"
import jittor_utils
cat_path = f"{jt.flags.cache_path}/cat.jpg"
print("download")
jittor_utils.download(cat_url, cat_path)
with open(cat_path, 'rb') as f:
img = Image.open(f).convert('RGB')
img = jt.array(np.array(img))
print(img.shape, img.dtype)
img = ((img.float() - 128) / 255).transpose(2, 0, 1)
with jt.flag_scope(trace_py_var=2, trace_var_data=1):
img = img[None, ...]
resnet18 = resnet.Resnet18(pretrained=True)
x = jt.float32(img)
y = resnet18(x)
y.sync()
data = jt.dump_trace_data()
jt.clear_trace_data()
with open(f"{jt.flags.cache_path}/resnet_with_feature.pkl",
"wb") as f:
pickle.dump(data, f)
for k, v in data["execute_op_info"].items():
for i in v['fused_ops']:
if i not in data["node_data"]:
assert 0, (i, "not found")
def test_resnet(self):
with jt.flag_scope(trace_py_var=2):
resnet18 = resnet.Resnet18()
x = jt.float32(np.random.rand(2, 3, 224, 224))
y = resnet18(x)
y.sync()
data = jt.dump_trace_data()
jt.clear_trace_data()
def test_simple_model(self):
with jt.flag_scope(trace_py_var=2):
model = Model(input_size=1)
batch_size = 10
x = jt.float32(np.random.rand(batch_size, 1))
y = model(x)
y.sync()
data = jt.dump_trace_data()
jt.clear_trace_data()
def test_resnet_train(self):
with jt.flag_scope(trace_py_var=2):
resnet18 = resnet.Resnet18()
opt = jt.optim.SGD(resnet18.parameters(), 0.1)
x = jt.float32(np.random.rand(2, 3, 224, 224))
y = resnet18(x)
opt.step(y**2)
jt.sync_all()
data = jt.dump_trace_data()
jt.clear_trace_data()
def test_simple_model(self):
with jt.flag_scope(trace_py_var=2):
model = Model(input_size=1)
batch_size = 10
x = jt.float32(np.random.rand(batch_size, 1))
y = model(x)
y.sync()
data = jt.dump_trace_data()
jt.clear_trace_data()
with open(f"{jt.flags.cache_path}/simple_model.pkl", "wb") as f:
pickle.dump(data, f)
def test_simple_model_train(self):
with jt.flag_scope(trace_py_var=2):
model = Model(input_size=1)
opt = jt.optim.SGD(model.parameters(), 0.1)
batch_size = 10
x = jt.float32(np.random.rand(batch_size, 1))
y = model(x)
opt.step(y**2)
jt.sync_all()
data = jt.dump_trace_data()
jt.clear_trace_data()
def test_resnet_infer(self):
with jt.flag_scope(trace_py_var=2):
resnet18 = resnet.Resnet18()
x = jt.float32(np.random.rand(2, 3, 224, 224))
y = resnet18(x)
y.sync()
data = jt.dump_trace_data()
jt.clear_trace_data()
with open(f"{jt.flags.cache_path}/resnet.pkl", "wb") as f:
pickle.dump(data, f)
for k, v in data["execute_op_info"].items():
for i in v['fused_ops']:
if i not in data["node_data"]:
assert 0, (i, "not found")
def test_resnet_trainx(self):
with jt.flag_scope(trace_py_var=2):
resnet18 = resnet.Resnet18()
opt = jt.optim.SGD(resnet18.parameters(), 0.1)
x = jt.float32(np.random.rand(2, 3, 224, 224))
y = resnet18(x)
opt.step(y**2)
jt.sync_all()
data = jt.dump_trace_data()
jt.clear_trace_data()
with open(f"{jt.flags.cache_path}/resnet_train.pkl", "wb") as f:
pickle.dump(data, f)
for k, v in data["execute_op_info"].items():
for i in v['fused_ops']:
if i not in data["node_data"]:
assert 0, (i, "not found")
for k, v in data["node_data"].items():
if 'name' not in v["attrs"]:
print(v)