code stringlengths 3 6.57k |
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Regressor(nn.Module) |
__init__(self, smpl_mean_params=SMPL_MEAN_PARAMS, feat_dim=2048, hidden_dim=1024, **kwargs) |
super(Regressor, self) |
__init__() |
nn.Linear(feat_dim + npose + nshape + 3, hidden_dim) |
nn.Dropout() |
nn.Linear(hidden_dim, hidden_dim) |
nn.Dropout() |
nn.Linear(hidden_dim, npose) |
nn.Linear(hidden_dim, nshape) |
nn.Linear(hidden_dim, 3) |
nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) |
nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) |
nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) |
np.load(smpl_mean_params) |
torch.from_numpy(mean_params['pose'][:]) |
unsqueeze(0) |
torch.from_numpy(mean_params['shape'][:].astype('float32') |
unsqueeze(0) |
torch.from_numpy(mean_params['cam']) |
unsqueeze(0) |
self.register_buffer('init_pose', init_pose) |
self.register_buffer('init_shape', init_shape) |
self.register_buffer('init_cam', init_cam) |
iterative_regress(self, x, init_pose=None, init_shape=None, init_cam=None, n_iter=3) |
self.init_pose.expand(nt, -1) |
self.init_shape.expand(nt, -1) |
self.init_cam.expand(nt, -1) |
range(n_iter) |
torch.cat([x, pred_pose, pred_shape, pred_cam], 1) |
self.fc1(xc) |
self.drop1(xc) |
self.fc2(xc) |
self.drop2(xc) |
self.decpose(xc) |
self.decshape(xc) |
self.deccam(xc) |
self.iterative_regress(x, init_pose, init_shape, init_cam, n_iter=3) |
self.get_output(pred_pose, pred_shape, pred_cam, J_regressor) |
get_output(self, pred_pose, pred_shape, pred_cam, J_regressor) |
rot6d_to_rotmat(pred_pose) |
reshape(nt, -1, 3, 3) |
unsqueeze(1) |
expand(pred_vertices.shape[0], -1, -1) |
to(pred_vertices.device) |
torch.matmul(J_regressor_batch, pred_vertices) |
projection(pred_joints, pred_cam) |
rotation_matrix_to_angle_axis(pred_rotmat.reshape(-1, 3, 3) |
reshape(nt, -1) |
torch.cat([pred_cam, pose, pred_shape], dim=1) |
projection(pred_joints, pred_camera) |
torch.zeros(batch_size, 2) |
torch.eye(3) |
unsqueeze(0) |
expand(batch_size, -1, -1) |
to(pred_joints.device) |
points (bs, N, 3) |
rotation (bs, 3, 3) |
translation (bs, 3) |
focal_length (bs,) |
camera_center (bs, 2) |
torch.zeros([batch_size, 3, 3], device=points.device) |
torch.einsum('bij,bkj->bki', rotation, points) |
translation.unsqueeze(1) |
unsqueeze(-1) |
torch.einsum('bij,bkj->bki', K, projected_points) |
information (system wide) |
get_bike_stations() |
spark.sql("""select distinct(station_id) |
toPandas() |
apply(lambda x: int(x) |
list(stationsOfInterestDF.values.flatten() |
dbutils.widgets.removeAll() |
dbutils.widgets.dropdown("00.Airport_Code", "JFK", ["JFK","SEA","BOS","ATL","LAX","SFO","DEN","DFW","ORD","CVG","CLT","DCA","IAH"]) |
dbutils.widgets.text('01.training_start_date', "2018-01-01") |
dbutils.widgets.text('02.training_end_date', "2019-03-15") |
dbutils.widgets.text('03.inference_date', (dt.strptime(str(dbutils.widgets.get('02.training_end_date') |
timedelta(days=1) |
strftime("%Y-%m-%d") |
dbutils.widgets.text('04.promote_model', "No") |
str(dbutils.widgets.get('01.training_start_date') |
str(dbutils.widgets.get('02.training_end_date') |
str(dbutils.widgets.get('03.inference_date') |
str(dbutils.widgets.get('00.Airport_Code') |
dbutils.widgets.get("05.promote_model") |
print(airport_code,training_start_date,training_end_date,inference_date,promote_model) |
MlflowClient() |
EXTRACT(year from a.hour) |
EXTRACT(dayofweek from a.hour) |
EXTRACT(hour from a.hour) |
COALESCE(c.tot_precip_mm,0) |
TO_DATE(a.hour) |
format(end_date, (datetime.strptime(end_date, '%Y-%m-%d') |
timedelta(hours=int(hours_to_forecast) |
strftime("%Y-%m-%d %H:%M:%S") |
fdf.toPandas() |
fillna(method='ffill') |
fillna(method='bfill') |
prod_model.predict(df1.drop(["ds","model"], axis=1) |
fdf.toPandas() |
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